REFMAP Listening test 1 Parts A & B analysis: Random forest variable importance identification - Revision 1
This notebook analyses both parts of the data in terms of variable importance, using a random forest model based on conditional inference trees and a conditional permutation variable importance algorithm.
Setup
Load packages
# load packages
library(tidyr)
library(ggplot2)
library(party)
library(conflicted)
library(tidyverse)
library(openxlsx)
library(caret)
library(viridis)
library(cowplot)
library(permimp)
# set package parameters
theme_set(theme_bw())
# plot colour scheme
mycolourlist = list(c(0, 102, 255), c(0, 204, 153), c(255, 0, 102), c(74, 111, 152), c(251, 164, 49), c(204, 153, 255), c(90, 192, 255), c(80, 245, 233), c(255, 90, 192), c(164, 201, 242), c(255, 254, 139), c(255, 243, 255))
mycolours = matrix()
for (ii in 1:length(mycolourlist)){
mycolours[ii] = rgb(mycolourlist[[ii]][1]/255,
mycolourlist[[ii]][2]/255,
mycolourlist[[ii]][3]/255)
}
# toggle to save plots
saveplots = TRUE
if (saveplots){
# set output plot directory
choose.files(caption="Just cancel this", filters=matrix(data=c(" ", " "), ncol=2)) # workaround for bug in RTerm choose.dir
outFigPath <- utils::choose.dir(caption="Select output folder to save plots '03 Experiment\\Experiment 1\\Analysis\\Plots'")
if (!dir.exists(file.path(outFigPath, "svg"))){dir.create(file.path(outFigPath, "svg"))}
if (!dir.exists(file.path(outFigPath, "pdf"))){dir.create(file.path(outFigPath, "pdf"))}
}
# toggle to save data
savedata = TRUE
if (savedata){
# set output plot directory
if (saveplots==FALSE){
choose.files(caption="Just cancel this", filters=matrix(data=c(" ", " "), ncol=2)) # workaround for bug in RTerm choose.dir
}
outDataPath <- utils::choose.dir(caption="Select output folder to save data '03 Experiment\\Experiment 1\\Analysis\\R'")
}
Import data and wrangle
stimDatapath <- utils::choose.files(caption=r"(Select refmap_listest1_testdata_ByStim.csv from 03 Experiment\Experiment 1\Analysis\PostProcess)",
filters=matrix(data=c("refmap_listest1_testdata_ByStim.csv", "refmap_listest1_testdata_ByStim.csv"), ncol=2))
stimData <- utils::read.csv(stimDatapath, header=TRUE)
colnames(stimData)[1] <- "Stimulus"
# make response proportions into percentages
stimData[['HighAnnoyPc']] <- stimData[['HighAnnoyProp']]*100
stimData[['dHighAnnoyPc']] <- stimData[['dHighAnnoyProp']]*100
# function to encode categorical to ordinal numeric variables
encode_ordinal <- function(x, order=unique(x)) {
x <- as.numeric(factor(x, levels=order, exclude=NULL, order=TRUE))
x
}
# definition of ordinal variable levels
SNRCats <- c("No UAS", "-16", "-10", "-4", "2", "8")
UASLAeqCats <- c("No UAS", "42", "48", "54", "60")
The aggregated data by stimulus are assigned to a dataframe, relevant categorical variables are converted to ordinal, and then the variable subset of interest is selected, NA rows dropped (ie, the ‘no UAS’ stimuli, as the conditional variable importance algorithm cannot currently handle NA values, which are present in all the UAS dB metrics), and a formula assigned.
stimDataNum <- data.frame()
stimDataNum <- cbind(stimData[, 'Stimulus'],
stimData[, "UASEvents"],
stimData[, which(colnames(stimData)=="UASLAeq"):
which(colnames(stimData)=="SNRlevel")],
stimData[, which(colnames(stimData)=="IntermitRatioC2MaxLR"):
which(colnames(stimData)=="IntermitRatioC5MaxLR")],
stimData[, which(colnames(stimData)=="UASLAEMaxLR"):
which(colnames(stimData)=="UASEPNLMaxLR")],
stimData[, which(colnames(stimData)=="UASLoudECMAPowAvgBin"):
which(colnames(stimData)=="UASLoudISO3PowAvgBin")],
stimData[, which(colnames(stimData)=="UASTonalECMAAvgMaxLR"):
which(colnames(stimData)=="UASSharpvBISO105ExBin")],
stimData[, which(colnames(stimData)=="UASImpulsSHMPowAvgMaxLR"):
which(colnames(stimData)=="UASPsychAnnoyBoucher")],
stimData[, which(colnames(stimData)=="LAeqLAF90diff"):
which(colnames(stimData)=="dPsychAnnoyBoucher")],
stimData[, which(colnames(stimData)=="ValenceMedian"):
which(colnames(stimData)=="dHighAnnoyProp")],
stimData[, which(colnames(stimData)=="HighAnnoyPc"):
which(colnames(stimData)=="dHighAnnoyPc")])
# remove duplicated variables
stimDataNum <- subset(stimDataNum, select = -c(UASLAeq))
colnames(stimDataNum)[1] <- "Stimulus"
colnames(stimDataNum)[2] <- "UASEvents"
# make the discrete ordinal outcome variables factors
stimDataNum[['UASEvents']] <- factor(stimDataNum[['UASEvents']], levels=c(0, 1, 3, 5, 9), order=TRUE)
stimDataNum[['ValenceMedian']] <- factor(stimDataNum[['ValenceMedian']], levels=c(1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5), order=TRUE)
stimDataNum[['ArousalMedian']] <- factor(stimDataNum[['ArousalMedian']], levels=c(1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5), order=TRUE)
stimDataNum[['AnnoyMedian']] <- factor(stimDataNum[['AnnoyMedian']], levels=c(0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5,
5.5, 6, 6.5, 7, 7.5, 8, 8.5, 9, 9.5, 10), order=TRUE)
stimDataNum[['dValenceMedian']] <- factor(stimDataNum[['dValenceMedian']], levels=c(-4, -3.5, -3, -2.5, -2, -1.5, -1, -0.5, 0,
0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4), order=TRUE)
stimDataNum[['dArousalMedian']] <- factor(stimDataNum[['dArousalMedian']], levels=c(-4, -3.5, -3, -2.5, -2, -1.5, -1, -0.5, 0,
0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4), order=TRUE)
stimDataNum[['dAnnoyMedian']] <- factor(stimDataNum[['dAnnoyMedian']], levels=c(-10, -9.5, -9, -8.5, -8, -7.5, -7, -6.5, -6, -5.5, -5,
-4.5, -4, -3.5, -3, -2.5, -2, -1.5, -1, -0.5,
0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5,
5.5, 6, 6.5, 7, 7.5, 8, 8.5, 9, 9.5, 10), order=TRUE)
# omit ambient-only stimuli
stimDataNum <- stimDataNum |> dplyr::filter(UASEvents != 0)
stimDataNum$SNRlevel <- as.numeric(stimDataNum$SNRlevel)Random forest functions
Write a function to train a conditional-inference random forest (crf) model on input data according to input formula, iterate over input random seeds, average error and variable importance metrics, and output metrics with plotted
Averaging over multiple random seeds
multi_crfReg <- function(dataIn, iVars, dVar, seeds, ntree, mtry, permImpCondThres=0.95, minsplit=20, minbucket=7, nperm=1){
# initialise variables
crfOOBErrAll <- 0
crfOOBRMSE <- 0
crfOOBMAE <- 0
crfOOBErrR2 <- 0
crfMarPermImpVals <- 0
crfConPermImpVals <- 0
crfMarPermImpValsPerTree <- data.frame()
crfConPermImpValsPerTree <- data.frame()
for (iters in 1:length(seeds)){
# formula for regression
formVars <- reformulate(iVars, dVar)
# set random seed
set.seed(seeds[iters])
# train crf model
crfModel <- party::cforest(formVars, data=dataIn,
controls=party::cforest_unbiased(ntree=ntree,
mtry=mtry,
minsplit=minsplit,
minbucket=minbucket))
# get OOB predictions
crfModelOOB <- predict(crfModel, OOB=TRUE, type='response')
# get OOB error
crfModelOOBErr <- as.numeric(as.matrix(as.numeric(as.matrix(crfModelOOB))
- as.numeric(as.matrix(crfModel@data@env$response[[names(crfModel@data@env$response)]]))))
# OOB RMSE, MAE and Rsquared
crfOOBRMSE <- crfOOBRMSE + sqrt(mean(crfModelOOBErr^2))
crfOOBMAE <- crfOOBMAE + mean(abs(crfModelOOBErr))
crfOOBErrR2 <- crfOOBErrR2 + cor(as.numeric(as.matrix(crfModelOOB)),
as.numeric(as.matrix(crfModel@data@env$response[[names(crfModel@data@env$response)]])))^2
# set random seed
set.seed(seeds[iters])
# set random seed
set.seed(seeds[iters])
# conditional variable permutation importance
crfConPermImp <- permimp::permimp(crfModel, nperm=nperm, conditional=TRUE, threshold=permImpCondThres, progressBar=FALSE)
crfConPermImpVals <- crfConPermImpVals + crfConPermImp$values
crfConPermImpValsPerTree <- rbind(crfConPermImpValsPerTree, crfConPermImp$perTree)
}
# average metrics
crfOOBErrAll <- crfOOBErrAll/length(seeds)
crfOOBRMSE <- crfOOBRMSE/length(seeds)
crfOOBMAE <- crfOOBMAE/length(seeds)
crfOOBErrR2 <- crfOOBErrR2/length(seeds)
crfConPermImpVals <- data.frame(CondPermImp=sort(crfConPermImpVals/length(seeds), decreasing=TRUE))
crfConPermImpValsQtl <- data.frame(apply(crfConPermImpValsPerTree, 2, quantile, probs=c(0.25, 0.50, 0.75)))
resultsOut <- list('OOB_RMSE'=crfOOBRMSE, 'OOB_MAE'=crfOOBMAE, 'Rsquared'=crfOOBErrR2, 'conditional_permimp'=crfConPermImpVals, 'conditional_permimp_perTree'=crfConPermImpValsPerTree, 'conditional_permimp_qtl'=crfConPermImpValsQtl)
return(resultsOut)
}
Comparing rankings from two seeds
crfReg <- function(dataIn, iVars, dVar, seeds, ntree, mtry, permImpCondThres=0.95, minsplit=20, minbucket=7, nperm=1){
# initialise variables
crfOOBErrAll <- 0
crfOOBRMSE <- 0
crfOOBMAE <- 0
crfOOBErrR2 <- 0
crfMarPermImpVals <- 0
crfConPermImpVals <- 0
crfMarPermImpValsPerTree <- data.frame()
crfConPermImpValsPerTree <- data.frame()
# formula for regression
formVars <- reformulate(iVars, dVar)
for (iters in 1:length(seeds)){
# set random seed
set.seed(seeds[iters])
# train crf model
crfModel <- party::cforest(formVars, data=dataIn,
controls=party::cforest_unbiased(ntree=ntree,
mtry=mtry,
minsplit=minsplit,
minbucket=minbucket))
# conditional variable permutation importance
crfConPermImp <- permimp::permimp(crfModel, nperm=nperm, conditional=TRUE, threshold=permImpCondThres, progressBar=FALSE)
crfConPermImpVals <- crfConPermImp$values
if (iters == 1){
crfConPermImpVals1 <- data.frame(CondPermImp=sort(crfConPermImpVals, decreasing=TRUE))
crfConPermImpValsPerTree1 <- crfConPermImp$perTree
crfConPermImpValsQtl1 <- data.frame(apply(crfConPermImpValsPerTree1, 2, quantile, probs=c(0.25, 0.50, 0.75)))
# get OOB predictions
crfModelOOB <- predict(crfModel, OOB=TRUE, type='response')
# get OOB error
crfModelOOBErr <- as.numeric(as.matrix(as.numeric(as.matrix(crfModelOOB))
- as.numeric(as.matrix(crfModel@data@env$response[[names(crfModel@data@env$response)]]))))
# OOB RMSE, error quartiles and Rsquared
crfOOBRMSE <- sqrt(mean(crfModelOOBErr^2))
crfOOBMAE <- crfOOBMAE + mean(abs(crfModelOOBErr))
crfOOBErrR2 <- cor(as.numeric(as.matrix(crfModelOOB)),
as.numeric(as.matrix(crfModel@data@env$response[[names(crfModel@data@env$response)]])))^2
}
else{
crfConPermImpValsN <- data.frame(CondPermImp=sort(crfConPermImpVals, decreasing=TRUE))
nVarImpChecks <- min(max(sum(crfConPermImpVals1 >= crfConPermImpVals1$CondPermImp[1]*0.1),
sum(crfConPermImpValsN >= crfConPermImpValsN$CondPermImp[1]*0.1)), 4) # number of variable importance values with a value at least 10% of the highest importance
if (sum(rownames(crfConPermImpVals1)[1:nVarImpChecks] != rownames(crfConPermImpValsN)[1:nVarImpChecks]) > 0){
warning("Permutation importance rank order within 10% of max differs between seeds: increase number of trees ('ntree') or number of permutations ('nperm'), or subsample of features ('mtry')")
}
else{
resultsOut <- list('OOB_errors'=crfModelOOBErr, 'OOB_RMSE'=crfOOBRMSE, 'OOB_MAE'=crfOOBMAE, 'Rsquared'=crfOOBErrR2, 'conditional_permimp'=crfConPermImpVals1, 'conditional_permimp_perTree'=crfConPermImpValsPerTree1, 'conditional_permpimp_qtl'=crfConPermImpValsQtl1)
return(resultsOut)
}
}
}
}
Hyperparameter tuning
mtryTune <- function(dataIn, iVars, dVar, seeds, ntrees, minsplit=20, minbucket=7){
formVars <- reformulate(iVars, dVar)
# set mtry values and corresponding iVars/mtry ratios
if (length(iVars) > 9){
iVars_mtrys <- c(10.5, 5.25, 3.5, 2.75, 2.25, 1.75, 1.5, 1.25)
mtrys <- as.integer(length(iVars)/iVars_mtrys)
}
else{
mtrys <- seq(2, length(iVars) - 3, by=1)
iVars_mtrys <- length(iVars)/mtrys
}
iVars_mtrys <- iVars_mtrys[mtrys >= 2] # remove 0 or 1 values
mtrys <- mtrys[mtrys >= 2] # remove 0 or 1 values
# remove any duplicated values
iVars_mtrys <- iVars_mtrys[!(duplicated(mtrys) | duplicated(mtrys, fromLast = TRUE))]
mtrys <- mtrys[!(duplicated(mtrys) | duplicated(mtrys, fromLast = TRUE))]
# ensure mtrys is less than length(iVars)
iVars_mtrys <- iVars_mtrys[mtrys < length(iVars)]
mtrys <- mtrys[mtrys < length(iVars)]
resRMSEMap = matrix(data=0, nrow=length(mtrys), ncol=length(ntrees))
resRsquaredMap = matrix(data=0, nrow=length(mtrys), ncol=length(ntrees))
resMAEMap = matrix(data=0, nrow=length(mtrys), ncol=length(ntrees))
for (ii in seq(1, length(ntrees))){
tuneMod.results <- data.frame(RMSE=numeric(length(mtrys)),
Rsquared=numeric(length(mtrys)),
MAE=numeric(length(mtrys)))
for (seed in seeds){
set.seed(seed)
ntree = ntrees[ii]
tuneMod <- caret::train(formVars,
data=dataIn,
method='cforest',
controls=party::cforest_unbiased(ntree=ntree,
minsplit=minsplit,
minbucket=minbucket),
tuneGrid=data.frame(.mtry=mtrys),
trControl = trainControl(method = "oob"))
# accumulate results
resRMSEMap[, ii] <- resRMSEMap[, ii] + tuneMod$results$RMSE
resRsquaredMap[, ii] <- resRsquaredMap[, ii] + tuneMod$results$Rsquared
resMAEMap[, ii] <- resMAEMap[, ii] + tuneMod$results$MAE
tuneMod.results <- tuneMod.results + tuneMod$results[, which(names(tuneMod$results) != 'mtry')]
}
# average results
tuneMod.results <- tuneMod.results/length(seeds)
tuneMod.results <- cbind(tuneMod.results, data.frame(mtry=mtrys), data.frame(iVars_mtry=iVars_mtrys))
print(tuneMod.results)
}
# average results
resRMSEMap <- resRMSEMap/length(seeds)
resRsquaredMap <- resRsquaredMap/length(seeds)
resMAEMap <- resMAEMap/length(seeds)
# convert to data frames with mtry as row names and ntree as column names, and convert to long format using tidyverse
resdfRMSEMap <- as.data.frame(resRMSEMap)
rownames(resdfRMSEMap) <- mtrys
colnames(resdfRMSEMap) <- ntrees
resdfRsquaredMap <- as.data.frame(resRsquaredMap)
rownames(resdfRsquaredMap) <- mtrys
colnames(resdfRsquaredMap) <- ntrees
resdfMAEMap <- as.data.frame(resMAEMap)
rownames(resdfMAEMap) <- mtrys
colnames(resdfMAEMap) <- ntrees
# convert dataframes to long format using tidyverse
resdfRMSEMap <- resdfRMSEMap |>
rownames_to_column('mtry') |>
gather(key='ntree', value='RMSE', -mtry)
resdfRsquaredMap <- resdfRsquaredMap |>
rownames_to_column('mtry') |>
gather(key='ntree', value='Rsquared', -mtry)
resdfMAEMap <- resdfMAEMap |>
rownames_to_column('mtry') |>
gather(key='ntree', value='MAE', -mtry)
# ensure ntree and mtry columns are ordered factors
resdfRMSEMap$ntree <- factor(resdfRMSEMap$ntree, levels=as.character(ntrees))
resdfRMSEMap$mtry <- factor(resdfRMSEMap$mtry, levels=as.character(mtrys))
resdfRsquaredMap$ntree <- factor(resdfRsquaredMap$ntree, levels=as.character(ntrees))
resdfRsquaredMap$mtry <- factor(resdfRsquaredMap$mtry, levels=as.character(mtrys))
resdfMAEMap$ntree <- factor(resdfMAEMap$ntree, levels=as.character(ntrees))
resdfMAEMap$mtry <- factor(resdfMAEMap$mtry, levels=as.character(mtrys))
# plot heatmaps using ggplot, with extreme (min or max) value plotted as overlaid point using annotate and colourbar scale reversed
pHeatmapRMSE <- ggplot(resdfRMSEMap) +
geom_tile(aes(x=ntree, y=mtry, fill=RMSE)) +
scale_fill_viridis(option="viridis", direction=-1) +
geom_point(data=resdfRMSEMap[which(resdfRMSEMap$RMSE
== min(resdfRMSEMap$RMSE),
arr.ind = TRUE),],
aes(x=ntree, y=mtry), colour="red", size=2) +
guides(colour = guide_colourbar(reverse=TRUE)) +
labs(x="ntree", y="mtry", fill="RMSE") +
theme(text = element_text(family = "serif"))
pHeatmapRsquared <- ggplot(resdfRsquaredMap) +
geom_tile(aes(x=ntree, y=mtry, fill=Rsquared)) +
scale_fill_viridis(option="viridis", direction=1) +
geom_point(data=resdfRsquaredMap[which(resdfRsquaredMap$Rsquared
== max(resdfRsquaredMap$Rsquared),
arr.ind = TRUE),],
aes(x=ntree, y=mtry), colour="red", size=2) +
guides(colour = guide_colourbar(reverse=TRUE)) +
labs(x="ntree", y="mtry", fill="Rsquared") +
theme(text = element_text(family = "serif"))
pHeatmapMAE <- ggplot(resdfMAEMap) +
geom_tile(aes(x=ntree, y=mtry, fill=MAE)) +
scale_fill_viridis(option="viridis", direction=-1) +
geom_point(data=resdfMAEMap[which(resdfMAEMap$MAE
== min(resdfMAEMap$MAE),
arr.ind = TRUE),],
aes(x=ntree, y=mtry), colour="red", size=2) +
guides(colour = guide_colourbar(reverse=TRUE)) +
labs(x="ntree", y="mtry", fill="MAE") +
theme(text = element_text(family = "serif"))
p <- cowplot::plot_grid(pHeatmapRMSE,
pHeatmapRsquared,
pHeatmapMAE,
ncol=3, nrow=1)
return(p)
} # end of function
Parts A & B analysis
Set global parameters
permImpCondThres <- 0.95
minsplit <- 20
minbucket <- 7
ntrees <- c(251, 501, 1001, 1501, 2501, 4001, 5501)
eventVar <- "UASEvents"
ambVar <- "AmbientLAeq"
Mean change in annoyance
Initialise results output variables
resdAnnoyMnFitAB <- data.frame(RMSE = numeric(),
MAE = numeric(),
Rsquared = numeric())
resdAnnoyMnPermImpAB <- list()
Absolute variables
Set variables
iVars <- names(stimDataNum)[which(names(stimDataNum) == 'UASEvents'):which(names(stimDataNum) == 'UASPsychAnnoyBoucher')]
iVars <- iVars[! iVars %in% c('SNRlevel', 'IntermitRatioC2MaxLR', 'IntermitRatioC3MaxLR', 'IntermitRatioC5MaxLR')]
dVar <- "dAnnoyMean"
seeds <- c(578312, 544, 84894, 54654, 153157)
Hyperparameter tuning
p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)p
if (saveplots){
ggsave(filename="PtsABdAnnoyMnAbsVarsHyperTune.svg", width=12, height=4, path=file.path(outFigPath, "svg"))
unlink("PtsABdAnnoyMnAbsVarsHyperTune.svg")
ggsave(filename="PtsABdAnnoyMnAbsVarsHyperTune.pdf", width=12, height=4, path=file.path(outFigPath, "pdf"))
unlink("PtsABdAnnoyMnAbsVarsHyperTune.pdf")
}
Selected hyperparameters
ntree <- 2501
mtry <- as.integer(length(iVars)/1.75)
Run model
Train preliminary model
nperm <- 5
resultsOutAbs <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutAbs$OOB_RMSE[1] 0.7179463resultsOutAbs$OOB_MAE[1] 0.5697517resultsOutAbs$Rsquared[1] 0.81913Train multiple seeds model
resultsOutAbs <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutAbs$OOB_RMSE[1] 0.7262005resultsOutAbs$OOB_MAE[1] 0.5771998resultsOutAbs$Rsquared[1] 0.8140447
# store results
resdAnnoyMnFitAB['Abs vars', 'RMSE'] <- resultsOutAbs$OOB_RMSE
resdAnnoyMnFitAB['Abs vars', 'MAE'] <- resultsOutAbs$OOB_MAE
resdAnnoyMnFitAB['Abs vars', 'Rsquared'] <- resultsOutAbs$Rsquared
resdAnnoyMnPermImpAB$AbsVars <- resultsOutAbs$conditional_permimp
Plot results
par(mai=c(0,3,0,0))
# plot conditional importance
resultsOutAbs.conimp <- arrange(resultsOutAbs$conditional_permimp, desc(row_number()))
pBar <- ggplot(resultsOutAbs.conimp) + geom_col(aes(x=factor(rownames(resultsOutAbs.conimp), levels=rownames(resultsOutAbs.conimp)), y=CondPermImp), fill=mycolours[1], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) +
coord_flip()
pBar
if (saveplots){
ggsave(filename="PtsABdAnnoyMnAbsVarsConPermimp.svg", width=8, height=13, path=file.path(outFigPath, "svg"))
unlink("PtsABdAnnoyMnAbsVarsConPermimp.svg")
ggsave(filename="PtsABdAnnoyMnAbsVarsConPermimp.pdf", width=8, height=13, path=file.path(outFigPath, "pdf"))
unlink("PtsABdAnnoyMnAbsVarsConPermimp.pdf")
}
# Plot only positive values
resultsOutAbs.conimpPtv <- resultsOutAbs.conimp |>
rownames_to_column('Metric') |>
filter_if(is.numeric, all_vars(. > 0)) |>
column_to_rownames('Metric')
pBar <- ggplot(resultsOutAbs.conimpPtv) + geom_col(aes(x=factor(rownames(resultsOutAbs.conimpPtv), levels=rownames(resultsOutAbs.conimpPtv)), y=CondPermImp), fill=mycolours[1], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar
if (saveplots){
ggsave(filename="PtsABdAnnoyMnAbsVarsConPermimpPtv.svg", width=8, height=10, path=file.path(outFigPath, "svg"))
unlink("PtsABdAnnoyMnAbsVarsConPermimpPtv.svg")
ggsave(filename="PtsABdAnnoyMnAbsVarsConPermimpPtv.pdf", width=8, height=10, path=file.path(outFigPath, "pdf"))
unlink("PtsABdAnnoyMnAbsVarsConPermimpPtv.pdf")
}
# Plot only values within 1% of the maximum
resultsOutAbs.conimp1pc <- resultsOutAbs.conimp |>
rownames_to_column('Metric') |>
filter_if(is.numeric, all_vars(. > max(resultsOutAbs.conimp)/100)) |>
column_to_rownames('Metric')
pBar <- ggplot(resultsOutAbs.conimp1pc) + geom_col(aes(x=factor(rownames(resultsOutAbs.conimp1pc), levels=rownames(resultsOutAbs.conimp1pc)), y=CondPermImp), fill=mycolours[1], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar
if (saveplots){
ggsave(filename="PtsABdAnnoyMnAbsVarsConPermimp1pc.svg", width=8, height=3, path=file.path(outFigPath, "svg"))
unlink("PtsABdAnnoyMnAbsVarsConPermimp1pc.svg")
ggsave(filename="PtsABdAnnoyMnAbsVarsConPermimp1pc.pdf", width=8, height=3, path=file.path(outFigPath, "pdf"))
unlink("PtsABdAnnoyMnAbsVarsConPermimp1pc.pdf")
}
Selected metric
absVar <- "UASLAEMaxLR"
SQM analysis
Individual SQMs
Sharpness
Set variables
iVars <- c(absVar, eventVar, ambVar, "UASSharpAurISO3PowAvgBin", "UASSharpAurISO305ExBin", "UASSharpAurSHMPowAvgBin", "UASSharpAurSHM05ExBin", "UASSharpAurISO1PowAvgBin", "UASSharpAurISO105ExBin", "UASSharpvBISO1PowAvgBin", "UASSharpvBISO105ExBin", "UASSharpDINPowAvgBin", "UASSharpDIN05ExBin", "UASSharpAurISO1MedBin",
"UASTonShpAurSHMPowAvgBin", "UASTonShpAurSHM05ExBin")
dVar <- "dAnnoyMean"
seeds <- c(7041, 905, 4984651, 6513213, 120651)
Hyperparameter tuning
p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)p
Selected hyperparameters
ntree <- 251
mtry <- as.integer(length(iVars)/1.5)
Run model
Train preliminary model
nperm <- 5
resultsOutSharp <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutSharp$OOB_RMSE[1] 0.6520071resultsOutSharp$OOB_MAE[1] 0.5246782resultsOutSharp$Rsquared[1] 0.8553724Train multiple seeds model
resultsOutSharp <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutSharp$OOB_RMSE[1] 0.6548829resultsOutSharp$OOB_MAE[1] 0.5229297resultsOutSharp$Rsquared[1] 0.8539235# store results
resdAnnoyMnFitAB['Abs sharp', 'RMSE'] <- resultsOutSharp$OOB_RMSE
resdAnnoyMnFitAB['Abs sharp', 'MAE'] <- resultsOutSharp$OOB_MAE
resdAnnoyMnFitAB['Abs sharp', 'Rsquared'] <- resultsOutSharp$Rsquared
resdAnnoyMnPermImpAB$AbsSharp <- resultsOutSharp$conditional_permimp
Plot results
par(mai=c(0,3,0,0))
# plot conditional importance
resultsOutSharp.conimp <- arrange(resultsOutSharp$conditional_permimp, desc(row_number()))
pBar <- ggplot(resultsOutSharp.conimp) + geom_col(aes(x=factor(rownames(resultsOutSharp.conimp), levels=rownames(resultsOutSharp.conimp)), y=CondPermImp), fill=mycolours[2], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + ggtitle("Sharpness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar
if (saveplots){
ggsave(filename="PtsABdAnnoyMnSharpConPermimp.svg", width=8, height=4.9, path=file.path(outFigPath, "svg"))
unlink("PtsABdAnnoyMnSharpConPermimp.svg")
ggsave(filename="PtsABdAnnoyMnSharpConPermimp.pdf", width=8, height=4.9, path=file.path(outFigPath, "pdf"))
unlink("PtsABdAnnoyMnSharpConPermimp.pdf")
}
Selected metric
sharpVar <- "UASSharpAurISO305ExBin"
Tonal loudness and tonality
Set variables
iVars <- c(absVar, eventVar, ambVar, "UASTonalECMAAvgMaxLR", "UASTonalSHMInt05ExMaxLR", "UASTonalSHMIntAvgMaxLR", "UASTonalECMA05ExMaxLR", "UASTonalAwSHMAvgMaxLR", "UASTonalAwSHM05ExMaxLR", "UASTonalAwSHMIntAvgMaxLR", "UASTonalAwSHMInt05ExMaxLR", "UASTonLdECMAPowAvgBin", "UASTonLdECMA05ExBin", "UASTonalAurAvgMaxLR", "UASTonalAur05ExMaxLR", "UASTonalAur10ExMaxLR",
"UASTonShpAurSHMPowAvgBin", "UASTonShpAurSHM05ExBin")
dVar <- "dAnnoyMean"
seeds <- c(540, 104798, 456464, 87331, 94564)
Hyperparameter tuning
p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)p
Selected hyperparameters
ntree <- 251
mtry <- as.integer(length(iVars)/1.5)
Run model
Train preliminary model
# Tonality with tonal loudness
nperm <- 5
resultsOutTonal1 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutTonal1$OOB_RMSE[1] 0.6817226resultsOutTonal1$OOB_MAE[1] 0.5354283resultsOutTonal1$Rsquared[1] 0.8328264Train multiple seeds model
# Tonality with tonal loudness
resultsOutTonal1 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutTonal1$OOB_RMSE[1] 0.6726346resultsOutTonal1$OOB_MAE[1] 0.5320356resultsOutTonal1$Rsquared[1] 0.8402319# store results
resdAnnoyMnFitAB['Abs tonal inc loud', 'RMSE'] <- resultsOutTonal1$OOB_RMSE
resdAnnoyMnFitAB['Abs tonal inc loud', 'MAE'] <- resultsOutTonal1$OOB_MAE
resdAnnoyMnFitAB['Abs tonal inc loud', 'Rsquared'] <- resultsOutTonal1$Rsquared
resdAnnoyMnPermImpAB$AbsTonal1 <- resultsOutTonal1$conditional_permimp
Plot results
par(mai=c(0,3,0,0))
# plot conditional importance
resultsOutTonal1.conimp <- arrange(resultsOutTonal1$conditional_permimp, desc(row_number()))
pBar <- ggplot(resultsOutTonal1.conimp) + geom_col(aes(x=factor(rownames(resultsOutTonal1.conimp), levels=rownames(resultsOutTonal1.conimp)), y=CondPermImp), fill=mycolours[3], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + ggtitle("Tonality inc. tonal loudness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 1.4))
pBar
if (saveplots){
ggsave(filename="PtsABdAnnoyMnTonalLdConPermimp.svg", width=8, height=5, path=file.path(outFigPath, "svg"))
unlink("PtsABdAnnoyMnTonalLdConPermimp.svg")
ggsave(filename="PtsABdAnnoyMnTonalLdConPermimp.pdf", width=8, height=5, path=file.path(outFigPath, "pdf"))
unlink("PtsABdAnnoyMnTonalLdConPermimp.pdf")
}
Selected metric
tonLdVar <- "UASTonLdECMAPowAvgBin"
Tonality without tonal loudness or tonal sharpness
Set variables
iVars <- c(absVar, eventVar, ambVar, "UASTonalECMAAvgMaxLR", "UASTonalSHMInt05ExMaxLR", "UASTonalSHMIntAvgMaxLR", "UASTonalECMA05ExMaxLR", "UASTonalAwSHMAvgMaxLR", "UASTonalAwSHM05ExMaxLR", "UASTonalAwSHMIntAvgMaxLR", "UASTonalAwSHMInt05ExMaxLR", "UASTonalAurAvgMaxLR", "UASTonalAur05ExMaxLR", "UASTonalAur10ExMaxLR")
dVar <- "dAnnoyMean"
seeds <- c(156089, 5860, 10528, 89541, 4685146)
Hyperparameter tuning
p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)p
Selected hyperparameters
ntree <- 501
mtry <- as.integer(length(iVars)/1.25)
Run model
Train preliminary model
# Tonality
nperm <- 5
resultsOutTonal2 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutTonal2$OOB_RMSE[1] 0.6735604resultsOutTonal2$OOB_MAE[1] 0.5215824resultsOutTonal2$Rsquared[1] 0.835001Train multiple seeds model
# Tonality
resultsOutTonal2 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutTonal2$OOB_RMSE[1] 0.6770415resultsOutTonal2$OOB_MAE[1] 0.526503resultsOutTonal2$Rsquared[1] 0.8322548
# store results
resdAnnoyMnFitAB['Abs tonal no loud', 'RMSE'] <- resultsOutTonal2$OOB_RMSE
resdAnnoyMnFitAB['Abs tonal no loud', 'MAE'] <- resultsOutTonal2$OOB_MAE
resdAnnoyMnFitAB['Abs tonal no loud', 'Rsquared'] <- resultsOutTonal2$Rsquared
resdAnnoyMnPermImpAB$AbsTonal2 <- resultsOutTonal2$conditional_permimp
Plot results
par(mai=c(0,3,0,0))
# plot conditional importance
resultsOutTonal2.conimp <- arrange(resultsOutTonal2$conditional_permimp, desc(row_number()))
pBar <- ggplot(resultsOutTonal2.conimp) + geom_col(aes(x=factor(rownames(resultsOutTonal2.conimp), levels=rownames(resultsOutTonal2.conimp)), y=CondPermImp), fill=mycolours[3], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + ggtitle("Tonality w/o tonal loudness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 1.4))
pBar
if (saveplots){
ggsave(filename="PtsABdAnnoyMnTonalConPermimp.svg", width=8, height=4.4, path=file.path(outFigPath, "svg"))
unlink("PtsABdAnnoyMnTonalConPermimp.svg")
ggsave(filename="PtsABdAnnoyMnTonalConPermimp.pdf", width=8, height=4.4, path=file.path(outFigPath, "pdf"))
unlink("PtsABdAnnoyMnTonalConPermimp.pdf")
}
Selected metric
tonalVar <- "UASTonalAwSHMInt05ExMaxLR"
Fluctuation strength
Set variables
# Fluctuation strength
iVars <- c(absVar, eventVar, ambVar, "UASFluctOldSHM10ExBin", "UASFluctOldSHM05ExBin", "UASFluctECMA10ExBin", "UASFluctECMA05ExBin", "UASFluctFZ10ExMaxLR", "UASFluctFZ05ExMaxLR", "UASFluctOV10ExMaxLR", "UASFluctOV05ExMaxLR")
dVar <- "dAnnoyMean"
seeds <- c(25107, 546098, 195, 5937, 102658)
Hyperparameter tuning
p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)p
Selected hyperparameters
ntree <- 5501
mtry <- as.integer(length(iVars)/1.5)
Run model
Train preliminary model
nperm <- 5
resultsOutFluct <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutFluct$OOB_RMSE[1] 0.6411068resultsOutFluct$OOB_MAE[1] 0.5139778resultsOutFluct$Rsquared[1] 0.8590107Train multiple seeds model
resultsOutFluct <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutFluct$OOB_RMSE[1] 0.6442208resultsOutFluct$OOB_MAE[1] 0.5160342resultsOutFluct$Rsquared[1] 0.8570714
# store results
resdAnnoyMnFitAB['Abs fluct', 'RMSE'] <- resultsOutFluct$OOB_RMSE
resdAnnoyMnFitAB['Abs fluct', 'MAE'] <- resultsOutFluct$OOB_MAE
resdAnnoyMnFitAB['Abs fluct', 'Rsquared'] <- resultsOutFluct$Rsquared
resdAnnoyMnPermImpAB$AbsFluct <- resultsOutFluct$conditional_permimp
Plot results
par(mai=c(0,3,0,0))
# plot conditional importance
resultsOutFluct.conimp <- arrange(resultsOutFluct$conditional_permimp, desc(row_number()))
pBar <- ggplot(resultsOutFluct.conimp) + geom_col(aes(x=factor(rownames(resultsOutFluct.conimp), levels=rownames(resultsOutFluct.conimp)), y=CondPermImp), fill=mycolours[4], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + ggtitle("Fluctuation strength") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar
if (saveplots){
ggsave(filename="PtsABdAnnoyMnFluctConPermimp.svg", width=8, height=3.5, path=file.path(outFigPath, "svg"))
unlink("PtsABdAnnoyMnFluctConPermimp.svg")
ggsave(filename="PtsABdAnnoyMnFluctConPermimp.pdf", width=8, height=3.5, path=file.path(outFigPath, "pdf"))
unlink("PtsABdAnnoyMnFluctConPermimp.pdf")
}
Selected metric
fluctVar <- "UASFluctECMA10ExBin"
Roughness
Set variables
# Roughness
iVars <- c(absVar, eventVar, ambVar, "UASRoughECMA10ExBin", "UASRoughECMA05ExBin", "UASRoughFZ10ExMaxLR", "UASRoughFZ05ExMaxLR", "UASRoughDW10ExMaxLR", "UASRoughDW05ExMaxLR")
dVar <- "dAnnoyMean"
seeds <- c(4701, 52187, 16589, 65217, 16893)
Hyperparameter tuning
p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)p
Selected hyperparameters
ntree <- 1001
mtry <- as.integer(length(iVars)/1.8)
Run model
Train preliminary model
nperm <- 5
resultsOutRough <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutRough$OOB_RMSE[1] 0.6741884resultsOutRough$OOB_MAE[1] 0.5361558resultsOutRough$Rsquared[1] 0.8539761Train multiple seeds model
resultsOutRough <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutRough$OOB_RMSE[1] 0.6614793resultsOutRough$OOB_MAE[1] 0.5271245resultsOutRough$Rsquared[1] 0.8617422# store results
resdAnnoyMnFitAB['Abs rough', 'RMSE'] <- resultsOutRough$OOB_RMSE
resdAnnoyMnFitAB['Abs rough', 'MAE'] <- resultsOutRough$OOB_MAE
resdAnnoyMnFitAB['Abs rough', 'Rsquared'] <- resultsOutRough$Rsquared
resdAnnoyMnPermImpAB$AbsRough <- resultsOutRough$conditional_permimp
Plot results
par(mai=c(0,3,0,0))
# plot conditional importance
resultsOutRough.conimp <- arrange(resultsOutRough$conditional_permimp, desc(row_number()))
pBar <- ggplot(resultsOutRough.conimp) + geom_col(aes(x=factor(rownames(resultsOutRough.conimp), levels=rownames(resultsOutRough.conimp)), y=CondPermImp), fill=mycolours[5], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + ggtitle("Roughness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar
if (saveplots){
ggsave(filename="PtsABdAnnoyMnRoughConPermimp.svg", width=8, height=2.9, path=file.path(outFigPath, "svg"))
unlink("PtsABdAnnoyMnRoughConPermimp.svg")
ggsave(filename="PtsABdAnnoyMnRoughConPermimp.pdf", width=8, height=2.9, path=file.path(outFigPath, "pdf"))
unlink("PtsABdAnnoyMnRoughConPermimp.pdf")
}
Selected metric
roughVar <- "UASRoughFZ05ExMaxLR"
Impulsiveness
Set variables
# Impulsiveness
iVars <- c(absVar, eventVar, ambVar, "UASImpulsSHMAvgMaxLR", "UASImpulsSHM05ExMaxLR", "UASImpulsSHMPowAvgMaxLR", "UASImpulsLoudWZAvgMaxLR", "UASImpulsLoudWZ05ExMaxLR", "UASImpulsLoudWZPowAvgMaxLR", "UASImpulsLoudWECMAAvgBin", "UASImpulsLoudWECMA05ExBin", "UASImpulsLoudWECMAPowAvgBin")
dVar <- "dAnnoyMean"
seeds <- c(8495, 59867, 5416, 9843, 86)
Hyperparameter tuning
p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)p
Selected hyperparameters
ntree <- 4001
mtry <- as.integer(length(iVars)/1.25)
Run model
Train preliminary model
nperm <- 5
resultsOutImpuls <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutImpuls$OOB_RMSE[1] 0.6721437resultsOutImpuls$OOB_MAE[1] 0.5263928resultsOutImpuls$Rsquared[1] 0.8394367Train multiple seeds model
resultsOutImpuls <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutImpuls$OOB_RMSE[1] 0.671666resultsOutImpuls$OOB_MAE[1] 0.525632resultsOutImpuls$Rsquared[1] 0.8396473
# store results
resdAnnoyMnFitAB['Abs impuls', 'RMSE'] <- resultsOutImpuls$OOB_RMSE
resdAnnoyMnFitAB['Abs impuls', 'MAE'] <- resultsOutImpuls$OOB_MAE
resdAnnoyMnFitAB['Abs impuls', 'Rsquared'] <- resultsOutImpuls$Rsquared
resdAnnoyMnPermImpAB$AbsImpuls <- resultsOutImpuls$conditional_permimp
Plot results
par(mai=c(0,3,0,0))
# plot conditional importance
resultsOutImpuls.conimp <- arrange(resultsOutImpuls$conditional_permimp, desc(row_number()))
pBar <- ggplot(resultsOutImpuls.conimp) + geom_col(aes(x=factor(rownames(resultsOutImpuls.conimp), levels=rownames(resultsOutImpuls.conimp)), y=CondPermImp), fill=mycolours[6], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + ggtitle("Impulsiveness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar
if (saveplots){
ggsave(filename="PtsABdAnnoyMnImpulsConPermimp.svg", width=8, height=3.8, path=file.path(outFigPath, "svg"))
unlink("PtsABdAnnoyMnImpulsConPermimp.svg")
ggsave(filename="PtsABdAnnoyMnImpulsConPermimp.pdf", width=8, height=3.8, path=file.path(outFigPath, "pdf"))
unlink("PtsABdAnnoyMnImpulsConPermimp.pdf")
}
Selected metric
impulsVar <- "UASImpulsLoudWZAvgMaxLR"
SQM and loudness comparison
Now the highest importance SQMs are ranked against each other, controlling for UAS loudness and ambient LAeq.
Include tonal loudness
Set variables
iVars <- c(absVar, eventVar, ambVar, sharpVar, tonLdVar, fluctVar, roughVar, impulsVar)
dVar <- "dAnnoyMean"
seeds <- c(70498, 4, 14986, 453, 864)
Hyperparameter tuning
p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)p
Selected hyperparameters
ntree <- 2501
mtry <- as.integer(length(iVars)/1.6)
Run model
Train preliminary model
nperm <- 5
resultsOutSQMs1 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutSQMs1$OOB_RMSE[1] 0.6690392resultsOutSQMs1$OOB_MAE[1] 0.5373742resultsOutSQMs1$Rsquared[1] 0.8465967Train multiple seeds model
resultsOutSQMs1 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutSQMs1$OOB_RMSE[1] 0.6651781resultsOutSQMs1$OOB_MAE[1] 0.5349845resultsOutSQMs1$Rsquared[1] 0.8487605
# store results
resdAnnoyMnFitAB['Abs SQMs inc tonal loud', 'RMSE'] <- resultsOutSQMs1$OOB_RMSE
resdAnnoyMnFitAB['Abs SQMs inc tonal loud', 'MAE'] <- resultsOutSQMs1$OOB_MAE
resdAnnoyMnFitAB['Abs SQMs inc tonal loud', 'Rsquared'] <- resultsOutSQMs1$Rsquared
resdAnnoyMnPermImpAB$AbsSQMs1 <- resultsOutSQMs1$conditional_permimp
Plot results
par(mai=c(0,3,0,0))
# plot conditional importance
resultsOutSQMs1.conimp <- arrange(resultsOutSQMs1$conditional_permimp, desc(row_number()))
pBar <- ggplot(resultsOutSQMs1.conimp) + geom_col(aes(x=factor(rownames(resultsOutSQMs1.conimp), levels=rownames(resultsOutSQMs1.conimp)), y=CondPermImp), fill=mycolours[7], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 1.3))
pBar
if (saveplots){
ggsave(filename="PtsABdAnnoyMnAbsSQMsTonLdConPermimp.svg", width=8, height=2.4, path=file.path(outFigPath, "svg"))
unlink("PtsABdAnnoyMnAbsSQMsTonLdConPermimp.svg")
ggsave(filename="PtsABdAnnoyMnAbsSQMsTonLdConPermimp.pdf", width=8, height=2.4, path=file.path(outFigPath, "pdf"))
unlink("PtsABdAnnoyMnAbsSQMsTonLdConPermimp.pdf")
}
Exclude tonal loudness
Set variables
iVars <- c(absVar, eventVar, ambVar, sharpVar, tonalVar, fluctVar, roughVar, impulsVar)
dVar <- "dAnnoyMean"
seeds <- c(546, 57203, 270835, 60592, 8094)
Hyperparameter tuning
p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)p
Selected hyperparameters
ntree <- 1501
mtry <- as.integer(length(iVars)/1.6)
Run model
Train preliminary model
nperm <- 5
resultsOutSQMs2 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutSQMs2$OOB_RMSE[1] 0.6741422resultsOutSQMs2$OOB_MAE[1] 0.5413668resultsOutSQMs2$Rsquared[1] 0.8439303Train multiple seeds model
resultsOutSQMs2 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutSQMs2$OOB_RMSE[1] 0.6606138resultsOutSQMs2$OOB_MAE[1] 0.5318176resultsOutSQMs2$Rsquared[1] 0.8522687
# store results
resdAnnoyMnFitAB['Abs SQMs no tonal loud', 'RMSE'] <- resultsOutSQMs2$OOB_RMSE
resdAnnoyMnFitAB['Abs SQMs no tonal loud', 'MAE'] <- resultsOutSQMs2$OOB_MAE
resdAnnoyMnFitAB['Abs SQMs no tonal loud', 'Rsquared'] <- resultsOutSQMs2$Rsquared
resdAnnoyMnPermImpAB$AbsSQMs2 <- resultsOutSQMs2$conditional_permimp
Plot results
par(mai=c(0,3,0,0))
# plot conditional importance
resultsOutSQMs2.conimp <- arrange(resultsOutSQMs2$conditional_permimp, desc(row_number()))
pBar <- ggplot(resultsOutSQMs2.conimp) + geom_col(aes(x=factor(rownames(resultsOutSQMs2.conimp), levels=rownames(resultsOutSQMs2.conimp)), y=CondPermImp), fill=mycolours[7], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 1.3))
pBar
if (saveplots){
ggsave(filename="PtsABdAnnoyMnAbsSQMsNoTonLdConPermimp.svg", width=8, height=2.4, path=file.path(outFigPath, "svg"))
unlink("PtsABdAnnoyMnAbsSQMsNoTonLdConPermimp.svg")
ggsave(filename="PtsABdAnnoyMnAbsSQMsNoTonLdConPermimp.pdf", width=8, height=2.4, path=file.path(outFigPath, "pdf"))
unlink("PtsABdAnnoyMnAbsSQMsNoTonLdConPermimp.pdf")
}
Psychoacoustic annoyance metrics
Set variables
iVars <- c(ambVar, "UASPsychAnnoyWidmann", "UASPsychAnnoyMore", "UASPsychAnnoyDi", "UASPsychAnnoyTorija", "UASPsychAnnoyWillemsen", "UASPsychAnnoyBoucher")
dVar <- "dAnnoyMean"
seeds <- c(829, 9, 190, 4564, 924707824)
Hyperparameter tuning
p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)p
Selected hyperparameters
ntree <- 1501
mtry <- as.integer(length(iVars)/1.75)
Run model
Train preliminary model
nperm <- 5
resultsOutPA <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutPA$OOB_RMSE[1] 0.6959625resultsOutPA$OOB_MAE[1] 0.5694917resultsOutPA$Rsquared[1] 0.8202226Train multiple seeds model
resultsOutPA <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutPA$OOB_RMSE[1] 0.6987601resultsOutPA$OOB_MAE[1] 0.5691674resultsOutPA$Rsquared[1] 0.8188134
# store results
resdAnnoyMnFitAB['Psychoacoustic annoyance', 'RMSE'] <- resultsOutPA$OOB_RMSE
resdAnnoyMnFitAB['Psychoacoustic annoyance', 'MAE'] <- resultsOutPA$OOB_MAE
resdAnnoyMnFitAB['Psychoacoustic annoyance', 'Rsquared'] <- resultsOutPA$Rsquared
resdAnnoyMnPermImpAB$AbsPA <- resultsOutPA$conditional_permimp
Plot results
par(mai=c(0,3,0,0))
# plot conditional importance
resultsOutPA.conimp <- arrange(resultsOutPA$conditional_permimp, desc(row_number()))
pBar <- ggplot(resultsOutPA.conimp) + geom_col(aes(x=factor(rownames(resultsOutPA.conimp), levels=rownames(resultsOutPA.conimp)), y=CondPermImp), fill=mycolours[10], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 1.8))
pBar
if (saveplots){
ggsave(filename="PtsABdAnnoyMnAbsPAConPermimp.svg", width=8, height=2.4, path=file.path(outFigPath, "svg"))
unlink("PtsABdAnnoyMnAbsPAConPermimp.svg")
ggsave(filename="PtsABdAnnoyMnAbsPAConPermimp.pdf", width=8, height=2.4, path=file.path(outFigPath, "pdf"))
unlink("PtsABdAnnoyMnAbsPAConPermimp.pdf")
}
All variables (absolute and difference)
Set variables
iVars <- names(stimDataNum)[which(names(stimDataNum) == 'UASEvents'):which(names(stimDataNum) == 'dPsychAnnoyBoucher')]
dVar <- "dAnnoyMean"
seeds <- c(14569, 98651, 54654498, 454948, 41321)
Hyperparameter tuning
p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)p
if (saveplots){
ggsave(filename="PtsABdAnnoyMnAllVarsHyperTune.svg", width=12, height=4, path=file.path(outFigPath, "svg"))
unlink("PtsABdAnnoyMnAllVarsHyperTune.svg")
ggsave(filename="PtsABdAnnoyMnAllVarsHyperTune.pdf", width=12, height=4, path=file.path(outFigPath, "pdf"))
unlink("PtsABdAnnoyMnAllVarsHyperTune.pdf")
}
Selected hyperparameters
ntree <- 2501
mtry <- as.integer(length(iVars)/3.5)
Run model
Train preliminary model
nperm <- 5
resultsOutAbsDiffs <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutAbsDiffs$OOB_RMSE[1] 0.5232516resultsOutAbsDiffs$OOB_MAE[1] 0.4044405resultsOutAbsDiffs$Rsquared[1] 0.8933325Train multiple seeds model
resultsOutAbsDiffs <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutAbsDiffs$OOB_RMSE[1] 0.5251036resultsOutAbsDiffs$OOB_MAE[1] 0.4057256resultsOutAbsDiffs$Rsquared[1] 0.8925424# store results
resdAnnoyMnFitAB['All vars', 'RMSE'] <- resultsOutAbsDiffs$OOB_RMSE
resdAnnoyMnFitAB['All vars', 'MAE'] <- resultsOutAbsDiffs$OOB_MAE
resdAnnoyMnFitAB['All vars', 'Rsquared'] <- resultsOutAbsDiffs$Rsquared
resdAnnoyMnPermImpAB$AllVars <- resultsOutAbsDiffs$conditional_permimp
Plot results
par(mai=c(0,3,0,0))
# plot conditional importance
resultsOutAbsDiffs.conimp <- arrange(resultsOutAbsDiffs$conditional_permimp, desc(row_number()))
pBar <- ggplot(resultsOutAbsDiffs.conimp) + geom_col(aes(x=factor(rownames(resultsOutAbsDiffs.conimp), levels=rownames(resultsOutAbsDiffs.conimp)), y=CondPermImp), fill=mycolours[9], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar
if (saveplots){
ggsave(filename="PtsABdAnnoyMnAllVarsConPermimp.svg", width=8, height=30, path=file.path(outFigPath, "svg"))
unlink("PtsABdAnnoyMnAllVarsConPermimp.svg")
ggsave(filename="PtsABdAnnoyMnAllVarsConPermimp.pdf", width=8, height=30, path=file.path(outFigPath, "pdf"))
unlink("PtsABdAnnoyMnAllVarsConPermimp.pdf")
}
# Plot only positive values
resultsOutAbsDiffs.conimpPtv <- resultsOutAbsDiffs.conimp |>
rownames_to_column('Metric') |>
filter_if(is.numeric, all_vars(. > 0)) |>
column_to_rownames('Metric')
pBar <- ggplot(resultsOutAbsDiffs.conimpPtv) + geom_col(aes(x=factor(rownames(resultsOutAbsDiffs.conimpPtv), levels=rownames(resultsOutAbsDiffs.conimpPtv)), y=CondPermImp), fill=mycolours[9], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar
if (saveplots){
ggsave(filename="PtsABdAnnoyMnAllVarsConPermimpPtv.svg", width=8, height=18, path=file.path(outFigPath, "svg"))
unlink("PtsABdAnnoyMnAllVarsConPermimp.svg")
ggsave(filename="PtsABdAnnoyMnAllVarsConPermimpPtv.pdf", width=8, height=18, path=file.path(outFigPath, "pdf"))
unlink("PtsABdAnnoyMnAllVarsConPermimp.pdf")
}
# Plot only values within 1% of the maximum
resultsOutAbsDiffs.conimp1pc <- resultsOutAbsDiffs.conimp |>
rownames_to_column('Metric') |>
filter_if(is.numeric, all_vars(. > max(resultsOutAbsDiffs.conimp)/100)) |>
column_to_rownames('Metric')
pBar <- ggplot(resultsOutAbsDiffs.conimp1pc) + geom_col(aes(x=factor(rownames(resultsOutAbsDiffs.conimp1pc), levels=rownames(resultsOutAbsDiffs.conimp1pc)), y=CondPermImp), fill=mycolours[9], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar
if (saveplots){
ggsave(filename="PtsABdAnnoyMnAllVarsConPermimp1pc.svg", width=8, height=6, path=file.path(outFigPath, "svg"))
unlink("PtsABdAnnoyMnAllVarsConPermimp1pc.svg")
ggsave(filename="PtsABdAnnoyMnAllVarsConPermimp1pc.pdf", width=8, height=6, path=file.path(outFigPath, "pdf"))
unlink("PtsABdAnnoyMnAllVarsConPermimp1pc.pdf")
}
Selected metric
allVar <- "Detect0p1dBIntMaxLR"
dSQM analysis
Individual SQMs
dSharpness
Set variables
iVars <- c(allVar, eventVar, ambVar, "dSharpAurISO3PowAvgBin", "dSharpAurISO305ExBin", "dSharpAurSHMPowAvgBin", "dSharpAurSHM05ExBin", "dTonShpAurSHMPowAvgBin", "dTonShpAurSHM05ExBin", "PartTonShpAurSHMPowAvgBin",
"PartTonShpAurSHM05ExBin", "UASSharpAurISO3PowAvgBin", "UASSharpAurISO305ExBin", "UASSharpAurSHMPowAvgBin", "UASSharpAurSHM05ExBin", "UASSharpAurISO1PowAvgBin", "UASSharpAurISO105ExBin", "UASSharpvBISO1PowAvgBin", "UASSharpvBISO105ExBin", "UASSharpDINPowAvgBin", "UASSharpDIN05ExBin", "UASSharpAurISO1MedBin",
"UASTonShpAurSHMPowAvgBin", "UASTonShpAurSHM05ExBin")
dVar <- "dAnnoyMean"
seeds <- c(84194, 905, 64815, 928054, 625091)
Hyperparameter tuning
p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)p
Selected hyperparameters
ntree <-251
mtry <- as.integer(length(iVars)/1.25)
Run model
Train preliminary model
nperm <- 5
resultsOutSharp <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutSharp$OOB_RMSE[1] 0.5170055resultsOutSharp$OOB_MAE[1] 0.3896635resultsOutSharp$Rsquared[1] 0.8955904Train multiple seeds model
resultsOutSharp <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutSharp$OOB_RMSE[1] 0.5117732resultsOutSharp$OOB_MAE[1] 0.3910043resultsOutSharp$Rsquared[1] 0.8983711
# store results
resdAnnoyMnFitAB['All sharp', 'RMSE'] <- resultsOutSharp$OOB_RMSE
resdAnnoyMnFitAB['All sharp', 'MAE'] <- resultsOutSharp$OOB_MAE
resdAnnoyMnFitAB['All sharp', 'Rsquared'] <- resultsOutSharp$Rsquared
resdAnnoyMnPermImpAB$AllSharp <- resultsOutSharp$conditional_permimp
Plot results
par(mai=c(0,3,0,0))
# plot conditional importance
resultsOutSharp.conimp <- arrange(resultsOutSharp$conditional_permimp, desc(row_number()))
pBar <- ggplot(resultsOutSharp.conimp) + geom_col(aes(x=factor(rownames(resultsOutSharp.conimp), levels=rownames(resultsOutSharp.conimp)), y=CondPermImp), fill=mycolours[2], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + ggtitle("All sharpness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar
if (saveplots){
ggsave(filename="PtsABdAnnoyMnAllSharpConPermimp.svg", width=8, height=5.4, path=file.path(outFigPath, "svg"))
unlink("PtsABdAnnoyMnAllSharpConPermimp.svg")
ggsave(filename="PtsABdAnnoyMnAllSharpConPermimp.pdf", width=8, height=5.4, path=file.path(outFigPath, "pdf"))
unlink("PtsABdAnnoyMnAllSharpConPermimp.pdf")
}
Selected metric
allSharpVar <- "dSharpAurISO3PowAvgBin"
dTonal loudness and dtonality
Set variables
iVars <- c(allVar, eventVar, ambVar, "dTonalECMAAvgMaxLR", "dTonalSHMInt05ExMaxLR", "dTonalSHMIntAvgMaxLR", "dTonalECMA05ExMaxLR", "dTonalAwSHMAvgMaxLR", "dTonalAwSHM05ExMaxLR", "dTonalAwSHMIntAvgMaxLR", "dTonalAwSHMInt05ExMaxLR", "dTonLdECMAPowAvgBin", "dTonLdECMA05ExBin", "dTonShpAurSHMPowAvgBin",
"dTonShpAurSHM05ExBin", "PartTonLdSHMPowAvgBin", "UASTonalECMAAvgMaxLR", "UASTonalSHMInt05ExMaxLR", "UASTonalSHMIntAvgMaxLR", "UASTonalECMA05ExMaxLR", "UASTonalAwSHMAvgMaxLR", "UASTonalAwSHM05ExMaxLR", "UASTonalAwSHMIntAvgMaxLR", "UASTonalAwSHMInt05ExMaxLR", "UASTonLdECMAPowAvgBin", "UASTonLdECMA05ExBin", "UASTonalAurAvgMaxLR", "UASTonalAur05ExMaxLR", "UASTonalAur10ExMaxLR",
"UASTonShpAurSHMPowAvgBin", "UASTonShpAurSHM05ExBin")
dVar <- "dAnnoyMean"
seeds <- c(561684, 104798, 1536, 48, 48561)
Hyperparameter tuning
p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)p
Selected hyperparameters
ntree <- 251
mtry <- as.integer(length(iVars)/1.25)
Run model
Train preliminary model
# Tonality with tonal loudness
nperm <- 5
resultsOutTonal1 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutTonal1$OOB_RMSE[1] 0.5151597resultsOutTonal1$OOB_MAE[1] 0.4039491resultsOutTonal1$Rsquared[1] 0.8960478Train multiple seeds model
# Tonality with tonal loudness
resultsOutTonal1 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutTonal1$OOB_RMSE[1] 0.5103953resultsOutTonal1$OOB_MAE[1] 0.4021889resultsOutTonal1$Rsquared[1] 0.8981797# store results
resdAnnoyMnFitAB['All tonal inc loud', 'RMSE'] <- resultsOutTonal1$OOB_RMSE
resdAnnoyMnFitAB['All tonal inc loud', 'MAE'] <- resultsOutTonal1$OOB_MAE
resdAnnoyMnFitAB['All tonal inc loud', 'Rsquared'] <- resultsOutTonal1$Rsquared
resdAnnoyMnPermImpAB$AllTonal1 <- resultsOutTonal1$conditional_permimp
Plot results
par(mai=c(0,3,0,0))
# plot conditional importance
resultsOutTonal1.conimp <- arrange(resultsOutTonal1$conditional_permimp, desc(row_number()))
pBar <- ggplot(resultsOutTonal1.conimp) + geom_col(aes(x=factor(rownames(resultsOutTonal1.conimp), levels=rownames(resultsOutTonal1.conimp)), y=CondPermImp), fill=mycolours[3], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + ggtitle("All tonality inc. tonal loudness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 2.2))
pBar
if (saveplots){
ggsave(filename="PtsABdAnnoyMnAllTonalLdConPermimp.svg", width=8, height=6, path=file.path(outFigPath, "svg"))
unlink("PtsABdAnnoyMnAllTonalLdConPermimp.svg")
ggsave(filename="PtsABdAnnoyMnAllTonalLdConPermimp.pdf", width=8, height=6, path=file.path(outFigPath, "pdf"))
unlink("PtsABdAnnoyMnAllTonalLdConPermimp.pdf")
}
Selected metric
allTonLdVar <- "dTonLdECMAPowAvgBin"
dTonality without dtonal loudness
Set variables
iVars <- c(allVar, eventVar, ambVar, "dTonalECMAAvgMaxLR", "dTonalSHMInt05ExMaxLR", "dTonalSHMIntAvgMaxLR", "dTonalECMA05ExMaxLR", "dTonalAwSHMAvgMaxLR", "dTonalAwSHM05ExMaxLR", "dTonalAwSHMIntAvgMaxLR", "dTonalAwSHMInt05ExMaxLR", "UASTonalECMAAvgMaxLR", "UASTonalSHMInt05ExMaxLR", "UASTonalSHMIntAvgMaxLR", "UASTonalECMA05ExMaxLR", "UASTonalAwSHMAvgMaxLR", "UASTonalAwSHM05ExMaxLR", "UASTonalAwSHMIntAvgMaxLR", "UASTonalAwSHMInt05ExMaxLR", "UASTonalAurAvgMaxLR", "UASTonalAur05ExMaxLR", "UASTonalAur10ExMaxLR")
dVar <- "dAnnoyMean"
seeds <- c(410865, 2954, 70812, 203, 7984)
Hyperparameter tuning
p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)p
Selected hyperparameters
ntree <- 501
mtry <- as.integer(length(iVars)/1.25)
Run model
Train preliminary model
# Tonality
nperm <- 5
resultsOutTonal2 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutTonal2$OOB_RMSE[1] 0.5010944resultsOutTonal2$OOB_MAE[1] 0.3841466resultsOutTonal2$Rsquared[1] 0.9029128Train multiple seeds model
# Tonality
resultsOutTonal2 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutTonal2$OOB_RMSE[1] 0.5000554resultsOutTonal2$OOB_MAE[1] 0.3831858resultsOutTonal2$Rsquared[1] 0.9033995
# store results
resdAnnoyMnFitAB['All tonal no loud', 'RMSE'] <- resultsOutTonal2$OOB_RMSE
resdAnnoyMnFitAB['All tonal no loud', 'MAE'] <- resultsOutTonal2$OOB_MAE
resdAnnoyMnFitAB['All tonal no loud', 'Rsquared'] <- resultsOutTonal2$Rsquared
resdAnnoyMnPermImpAB$AllTonal2 <- resultsOutTonal2$conditional_permimp
Plot results
par(mai=c(0,3,0,0))
# plot conditional importance
resultsOutTonal2.conimp <- arrange(resultsOutTonal2$conditional_permimp, desc(row_number()))
pBar <- ggplot(resultsOutTonal2.conimp) + geom_col(aes(x=factor(rownames(resultsOutTonal2.conimp), levels=rownames(resultsOutTonal2.conimp)), y=CondPermImp), fill=mycolours[3], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + ggtitle("All tonality w/o tonal loudness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 2.6))
pBar
if (saveplots){
ggsave(filename="PtsABdAnnoyMnAllTonalConPermimp.svg", width=8, height=5.8, path=file.path(outFigPath, "svg"))
unlink("PtsABdAnnoyMnAllTonalConPermimp.svg")
ggsave(filename="PtsABAllAnnoyMndTonalConPermimp.pdf", width=8, height=5.8, path=file.path(outFigPath, "pdf"))
unlink("PtsABdAnnoyMnAllTonalConPermimp.pdf")
}
Selected metric
allTonalVar <- "dTonalSHMIntAvgMaxLR"
dFluctuation strength
Set variables
# Fluctuation strength
iVars <- c(allVar, eventVar, ambVar, "dFluctECMA10ExBin", "dFluctECMA05ExBin", "dFluctOV10ExMaxLR", "dFluctOV05ExMaxLR", "UASFluctOldSHM10ExBin", "UASFluctOldSHM05ExBin", "UASFluctECMA10ExBin", "UASFluctECMA05ExBin", "UASFluctFZ10ExMaxLR", "UASFluctFZ05ExMaxLR", "UASFluctOV10ExMaxLR", "UASFluctOV05ExMaxLR")
dVar <- "dAnnoyMean"
seeds <- c(418657, 84, 1630, 18659, 3687)
Hyperparameter tuning
p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)p
Selected hyperparameters
ntree <- 4001
mtry <- as.integer(length(iVars)/1.5)
Run model
Train preliminary model
nperm <- 5
resultsOutFluct <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutFluct$OOB_RMSE[1] 0.4966309resultsOutFluct$OOB_MAE[1] 0.3732706resultsOutFluct$Rsquared[1] 0.9046225Train multiple seeds model
resultsOutFluct <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutFluct$OOB_RMSE[1] 0.4969742resultsOutFluct$OOB_MAE[1] 0.3744435resultsOutFluct$Rsquared[1] 0.9047033
# store results
resdAnnoyMnFitAB['All fluct', 'RMSE'] <- resultsOutFluct$OOB_RMSE
resdAnnoyMnFitAB['All fluct', 'MAE'] <- resultsOutFluct$OOB_MAE
resdAnnoyMnFitAB['All fluct', 'Rsquared'] <- resultsOutFluct$Rsquared
resdAnnoyMnPermImpAB$AllFluct <- resultsOutFluct$conditional_permimp
Plot results
par(mai=c(0,3,0,0))
# plot conditional importance
resultsOutFluct.conimp <- arrange(resultsOutFluct$conditional_permimp, desc(row_number()))
pBar <- ggplot(resultsOutFluct.conimp) + geom_col(aes(x=factor(rownames(resultsOutFluct.conimp), levels=rownames(resultsOutFluct.conimp)), y=CondPermImp), fill=mycolours[4], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + ggtitle("All fluctuation strength") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar
if (saveplots){
ggsave(filename="PtsABdAnnoyMnAllFluctConPermimp.svg", width=8, height=4, path=file.path(outFigPath, "svg"))
unlink("PtsABdAnnoyMnAllFluctConPermimp.svg")
ggsave(filename="PtsABdAnnoyMnAllFluctConPermimp.pdf", width=8, height=4, path=file.path(outFigPath, "pdf"))
unlink("PtsABdAnnoyMnAllFluctConPermimp.pdf")
}
Selected metric
allFluctVar <- "dFluctECMA10ExBin"
dRoughness
Set variables
# Roughness
iVars <- c(allVar, eventVar, ambVar, "dRoughECMA10ExBin", "dRoughECMA05ExBin", "dRoughFZ10ExMaxLR", "dRoughFZ05ExMaxLR", "UASRoughECMA10ExBin", "UASRoughECMA05ExBin", "UASRoughFZ10ExMaxLR", "UASRoughFZ05ExMaxLR", "UASRoughDW10ExMaxLR", "UASRoughDW05ExMaxLR")
dVar <- "dAnnoyMean"
seeds <- c(69851, 85109, 410986, 1563, 896)
Hyperparameter tuning
p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)p
Selected hyperparameters
ntree <- 1001
mtry <- as.integer(length(iVars)/1.25)
Run model
Train preliminary model
nperm <- 5
resultsOutRough <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutRough$OOB_RMSE[1] 0.5060503resultsOutRough$OOB_MAE[1] 0.3812809resultsOutRough$Rsquared[1] 0.9036606Train multiple seeds model
resultsOutRough <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutRough$OOB_RMSE[1] 0.5099848resultsOutRough$OOB_MAE[1] 0.3855287resultsOutRough$Rsquared[1] 0.9015348# store results
resdAnnoyMnFitAB['All rough', 'RMSE'] <- resultsOutRough$OOB_RMSE
resdAnnoyMnFitAB['All rough', 'MAE'] <- resultsOutRough$OOB_MAE
resdAnnoyMnFitAB['All rough', 'Rsquared'] <- resultsOutRough$Rsquared
resdAnnoyMnPermImpAB$AllRough <- resultsOutRough$conditional_permimp
Plot results
par(mai=c(0,3,0,0))
# plot conditional importance
resultsOutRough.conimp <- arrange(resultsOutRough$conditional_permimp, desc(row_number()))
pBar <- ggplot(resultsOutRough.conimp) + geom_col(aes(x=factor(rownames(resultsOutRough.conimp), levels=rownames(resultsOutRough.conimp)), y=CondPermImp), fill=mycolours[5], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + ggtitle("All roughness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar
if (saveplots){
ggsave(filename="PtsABdAnnoyMnAllRoughConPermimp.svg", width=8, height=4, path=file.path(outFigPath, "svg"))
unlink("PtsABdAnnoyMnAllRoughConPermimp.svg")
ggsave(filename="PtsABdAnnoyMnAllRoughConPermimp.pdf", width=8, height=4, path=file.path(outFigPath, "pdf"))
unlink("PtsABdAnnoyMnAllRoughConPermimp.pdf")
}
Selected metric
allRoughVar <- "dRoughFZ05ExMaxLR"
dImpulsiveness
Set variables
# Impulsiveness
iVars <- c(allVar, eventVar, ambVar, "dImpulsSHMAvgMaxLR", "dImpulsSHM05ExMaxLR", "dImpulsSHMPowAvgMaxLR",
"dImpulsLoudWZAvgMaxLR", "dImpulsLoudWZ05ExMaxLR", "dImpulsLoudWZPowAvgMaxLR",
"dImpulsLoudWECMAAvgBin", "dImpulsLoudWECMA05ExBin", "dImpulsLoudWECMAPowAvgBin", "UASImpulsSHMAvgMaxLR", "UASImpulsSHM05ExMaxLR", "UASImpulsSHMPowAvgMaxLR", "UASImpulsLoudWZAvgMaxLR", "UASImpulsLoudWZ05ExMaxLR", "UASImpulsLoudWZPowAvgMaxLR", "UASImpulsLoudWECMAAvgBin", "UASImpulsLoudWECMA05ExBin", "UASImpulsLoudWECMAPowAvgBin")
dVar <- "dAnnoyMean"
seeds <- c(418659, 7805, 38475, 65834, 1653)
Hyperparameter tuning
p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)p
Selected hyperparameters
ntree <- 501
mtry <- as.integer(length(iVars)/1.25)
Run model
Train preliminary model
nperm <- 5
resultsOutImpuls <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutImpuls$OOB_RMSE[1] 0.5081669resultsOutImpuls$OOB_MAE[1] 0.3888782resultsOutImpuls$Rsquared[1] 0.9001747Train multiple seeds model
resultsOutImpuls <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutImpuls$OOB_RMSE[1] 0.5077729resultsOutImpuls$OOB_MAE[1] 0.3904791resultsOutImpuls$Rsquared[1] 0.9006062
# store results
resdAnnoyMnFitAB['All impuls', 'RMSE'] <- resultsOutImpuls$OOB_RMSE
resdAnnoyMnFitAB['All impuls', 'MAE'] <- resultsOutImpuls$OOB_MAE
resdAnnoyMnFitAB['All impuls', 'Rsquared'] <- resultsOutImpuls$Rsquared
resdAnnoyMnPermImpAB$AllImpuls <- resultsOutImpuls$conditional_permimp
Plot results
par(mai=c(0,3,0,0))
# plot conditional importance
resultsOutImpuls.conimp <- arrange(resultsOutImpuls$conditional_permimp, desc(row_number()))
pBar <- ggplot(resultsOutImpuls.conimp) + geom_col(aes(x=factor(rownames(resultsOutImpuls.conimp), levels=rownames(resultsOutImpuls.conimp)), y=CondPermImp), fill=mycolours[6], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + ggtitle("All impulsiveness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar
if (saveplots){
ggsave(filename="PtsABdAnnoyMnAllImpulsConPermimp.svg", width=8, height=5.6, path=file.path(outFigPath, "svg"))
unlink("PtsABdAnnoyMnAllImpulsConPermimp.svg")
ggsave(filename="PtsABdAnnoyMnAllImpulsConPermimp.pdf", width=8, height=5.6, path=file.path(outFigPath, "pdf"))
unlink("PtsABdAnnoyMnAllImpulsConPermimp.pdf")
}
Selected metric
allImpulsVar <- "dImpulsLoudWZAvgMaxLR"
dSQM and loudness comparison
Now the highest importance dSQMs are ranked against each other, controlling for loudness difference.
Include dtonal loudness
Set variables
iVars <- c(allVar, eventVar, ambVar, allSharpVar, allTonLdVar, allFluctVar, allRoughVar, allImpulsVar)
dVar <- "dAnnoyMean"
seeds <- c(98465, 54163, 6541, 36485, 849675)
Hyperparameter tuning
p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)p
Selected hyperparameters
ntree <- 501
mtry <- as.integer(length(iVars)/2)
Run model
Train preliminary model
nperm <- 5
resultsOutSQMs1 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutSQMs1$OOB_RMSE[1] 0.4971808resultsOutSQMs1$OOB_MAE[1] 0.3860652resultsOutSQMs1$Rsquared[1] 0.9059535Train multiple seeds model
resultsOutSQMs1 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutSQMs1$OOB_RMSE[1] 0.4991241resultsOutSQMs1$OOB_MAE[1] 0.3861392resultsOutSQMs1$Rsquared[1] 0.904781
# store results
resdAnnoyMnFitAB['All SQMs inc tonal loud', 'RMSE'] <- resultsOutSQMs1$OOB_RMSE
resdAnnoyMnFitAB['All SQMs inc tonal loud', 'MAE'] <- resultsOutSQMs1$OOB_MAE
resdAnnoyMnFitAB['All SQMs inc tonal loud', 'Rsquared'] <- resultsOutSQMs1$Rsquared
resdAnnoyMnPermImpAB$AllSQMs1 <- resultsOutSQMs1$conditional_permimp
Plot results
par(mai=c(0,3,0,0))
# plot conditional importance
resultsOutSQMs1.conimp <- arrange(resultsOutSQMs1$conditional_permimp, desc(row_number()))
pBar <- ggplot(resultsOutSQMs1.conimp) + geom_col(aes(x=factor(rownames(resultsOutSQMs1.conimp), levels=rownames(resultsOutSQMs1.conimp)), y=CondPermImp), fill=mycolours[7], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 2))
pBar
if (saveplots){
ggsave(filename="PtsABdAnnoyMnAllSQMsTonLdConPermimp.svg", width=8, height=2.4, path=file.path(outFigPath, "svg"))
unlink("PtsABdAnnoyMnAllSQMsTonLdConPermimp.svg")
ggsave(filename="PtsABdAnnoyMnAllSQMsTonLdConPermimp.pdf", width=8, height=2.4, path=file.path(outFigPath, "pdf"))
unlink("PtsABdAnnoyMnAllSQMsTonLdConPermimp.pdf")
}
Exclude tonal loudness
Set variables
iVars <- c(allVar, eventVar, ambVar, allSharpVar, allTonalVar, allFluctVar, allRoughVar, allImpulsVar)
dVar <- "dAnnoyMean"
seeds <- c(49865, 7852, 845961, 410583, 36748)
Hyperparameter tuning
p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)p
Selected hyperparameters
ntree <- 2501
mtry <- as.integer(length(iVars)/1.6)
Run model
Train preliminary model
nperm <- 5
resultsOutSQMs2 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutSQMs2$OOB_RMSE[1] 0.4906752resultsOutSQMs2$OOB_MAE[1] 0.3786122resultsOutSQMs2$Rsquared[1] 0.9089686Train multiple seeds model
resultsOutSQMs2 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutSQMs2$OOB_RMSE[1] 0.4897326resultsOutSQMs2$OOB_MAE[1] 0.376146resultsOutSQMs2$Rsquared[1] 0.9091913
# store results
resdAnnoyMnFitAB['All SQMs no tonal loud', 'RMSE'] <- resultsOutSQMs2$OOB_RMSE
resdAnnoyMnFitAB['All SQMs no tonal loud', 'MAE'] <- resultsOutSQMs2$OOB_MAE
resdAnnoyMnFitAB['All SQMs no tonal loud', 'Rsquared'] <- resultsOutSQMs2$Rsquared
resdAnnoyMnPermImpAB$AllSQMs2 <- resultsOutSQMs2$conditional_permimp
Plot results
par(mai=c(0,3,0,0))
# plot conditional importance
resultsOutSQMs2.conimp <- arrange(resultsOutSQMs2$conditional_permimp, desc(row_number()))
pBar <- ggplot(resultsOutSQMs2.conimp) + geom_col(aes(x=factor(rownames(resultsOutSQMs2.conimp), levels=rownames(resultsOutSQMs2.conimp)), y=CondPermImp), fill=mycolours[7], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 2))
pBar
if (saveplots){
ggsave(filename="PtsABdAnnoyMnAllSQMsNoTonLdConPermimp.svg", width=8, height=2.4, path=file.path(outFigPath, "svg"))
unlink("PtsABdAnnoyMnAllSQMsNoTonLdConPermimp.svg")
ggsave(filename="PtsABdAnnoyMnAllSQMsNoTonLdConPermimp.pdf", width=8, height=2.4, path=file.path(outFigPath, "pdf"))
unlink("PtsABdAnnoyMnAllSQMsNoTonLdConPermimp.pdf")
}
dPsychoacoustic annoyance metrics
Set variables
iVars <- c(ambVar, "dPsychAnnoyWidmann", "dPsychAnnoyMore", "dPsychAnnoyDi", "dPsychAnnoyTorija", "dPsychAnnoyWillemsen", "dPsychAnnoyBoucher", "UASPsychAnnoyWidmann", "UASPsychAnnoyMore", "UASPsychAnnoyDi", "UASPsychAnnoyTorija", "UASPsychAnnoyWillemsen", "UASPsychAnnoyBoucher")
dVar <- "dAnnoyMean"
seeds <- c(47896643, 475, 654, 98987132, 5446)
Hyperparameter tuning
p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)p
Selected hyperparameters
ntree <- 4001
mtry <- as.integer(length(iVars)/1.25)
Run model
Train preliminary model
nperm <- 5
resultsOutPA <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutPA$OOB_RMSE[1] 0.7066857resultsOutPA$OOB_MAE[1] 0.5544466resultsOutPA$Rsquared[1] 0.8031693Train multiple seeds model
resultsOutPA <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutPA$OOB_RMSE[1] 0.7071518resultsOutPA$OOB_MAE[1] 0.5548064resultsOutPA$Rsquared[1] 0.8028069
# store results
resdAnnoyMnFitAB['All Psychoacoustic annoyance', 'RMSE'] <- resultsOutPA$OOB_RMSE
resdAnnoyMnFitAB['All Psychoacoustic annoyance', 'MAE'] <- resultsOutPA$OOB_MAE
resdAnnoyMnFitAB['All Psychoacoustic annoyance', 'Rsquared'] <- resultsOutPA$Rsquared
resdAnnoyMnPermImpAB$AllPA <- resultsOutPA$conditional_permimp
Plot results
par(mai=c(0,3,0,0))
# plot conditional importance
resultsOutPA.conimp <- arrange(resultsOutPA$conditional_permimp, desc(row_number()))
pBar <- ggplot(resultsOutPA.conimp) + geom_col(aes(x=factor(rownames(resultsOutPA.conimp), levels=rownames(resultsOutPA.conimp)), y=CondPermImp), fill=mycolours[10], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 1.8))
pBar
if (saveplots){
ggsave(filename="PtsABdAnnoyMnAllPAConPermimp.svg", width=8, height=4, path=file.path(outFigPath, "svg"))
unlink("PtsABdAnnoyMnAllPAConPermimp.svg")
ggsave(filename="PtsABdAnnoyMnAllPAConPermimp.pdf", width=8, height=4, path=file.path(outFigPath, "pdf"))
unlink("PtsABdAnnoyMnAllPAConPermimp.pdf")
}
Save the results outputs to file
if (savedata){
utils::write.csv(resdAnnoyMnFitAB, paste(outDataPath, "\\PtsABCRFdAnnoyMnOOBFit.csv", sep=""))
ii <- 0
temp = list()
for (res in resdAnnoyMnPermImpAB){
ii <- ii + 1
temp[[ii]] <- as.data.frame(resdAnnoyMnPermImpAB[ii])
names(temp[[ii]]) <- names(resdAnnoyMnPermImpAB[ii])
}
openxlsx::write.xlsx(temp, paste(outDataPath, "\\PtsABCRFdAnnoyMnConPermimp.xlsx",
sep=""),
rowNames=TRUE)
}
(Change to) High annoyance
Initialise results output variables
resdHiAnnoyFitAB <- data.frame(RMSE = numeric(),
MAE = numeric(),
Rsquared = numeric())
resdHiAnnoyPermImpAB <- list()
Absolute variables
Set variables
iVars <- names(stimDataNum)[which(names(stimDataNum) == 'UASEvents'):which(names(stimDataNum) == 'UASPsychAnnoyBoucher')]
iVars <- iVars[! iVars %in% c('SNRlevel', 'IntermitRatioC2MaxLR', 'IntermitRatioC3MaxLR', 'IntermitRatioC5MaxLR')]
dVar <- "dHighAnnoyPc"
seeds <- c(578312, 544, 84894, 54654, 153157)
Hyperparameter tuning
p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)p
if (saveplots){
ggsave(filename="PtsABdHiAnnoyAbsVarsHyperTune.svg", width=12, height=4, path=file.path(outFigPath, "svg"))
unlink("PtsABdHiAnnoyAbsVarsHyperTune.svg")
ggsave(filename="PtsABdHiAnnoyAbsVarsHyperTune.pdf", width=12, height=4, path=file.path(outFigPath, "pdf"))
unlink("PtsABdHiAnnoyAbsVarsHyperTune.pdf")
}
Selected hyperparameters
ntree <- 1501
mtry <- as.integer(length(iVars)/1.75)
Run model
Train preliminary model
nperm <- 5
resultsOutAbs <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutAbs$OOB_RMSE[1] 6.203493resultsOutAbs$OOB_MAE[1] 4.787191resultsOutAbs$Rsquared[1] 0.6664964Train multiple seeds model
resultsOutAbs <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutAbs$OOB_RMSE[1] 6.205791resultsOutAbs$OOB_MAE[1] 4.775969resultsOutAbs$Rsquared[1] 0.6664051
# store results
resdHiAnnoyFitAB['Abs vars', 'RMSE'] <- resultsOutAbs$OOB_RMSE
resdHiAnnoyFitAB['Abs vars', 'MAE'] <- resultsOutAbs$OOB_MAE
resdHiAnnoyFitAB['Abs vars', 'Rsquared'] <- resultsOutAbs$Rsquared
resdHiAnnoyPermImpAB$AbsVars <- resultsOutAbs$conditional_permimp
Plot results
par(mai=c(0,3,0,0))
# plot conditional importance
resultsOutAbs.conimp <- arrange(resultsOutAbs$conditional_permimp, desc(row_number()))
pBar <- ggplot(resultsOutAbs.conimp) + geom_col(aes(x=factor(rownames(resultsOutAbs.conimp), levels=rownames(resultsOutAbs.conimp)), y=CondPermImp), fill=mycolours[1], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) +
coord_flip()
pBar
if (saveplots){
ggsave(filename="PtsABdHiAnnoyAbsVarsConPermimp.svg", width=8, height=14, path=file.path(outFigPath, "svg"))
unlink("PtsABdHiAnnoyAbsVarsConPermimp.svg")
ggsave(filename="PtsABdHiAnnoyAbsVarsConPermimp.pdf", width=8, height=14, path=file.path(outFigPath, "pdf"))
unlink("PtsABdHiAnnoyAbsVarsConPermimp.pdf")
}
# Plot only positive values
resultsOutAbs.conimpPtv <- resultsOutAbs.conimp |>
rownames_to_column('Metric') |>
filter_if(is.numeric, all_vars(. > 0)) |>
column_to_rownames('Metric')
pBar <- ggplot(resultsOutAbs.conimpPtv,) + geom_col(aes(x=factor(rownames(resultsOutAbs.conimpPtv), levels=rownames(resultsOutAbs.conimpPtv)), y=CondPermImp), fill=mycolours[1], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar
if (saveplots){
ggsave(filename="PtsABdHiAnnoyAbsVarsConPermimpPtv.svg", width=8, height=10, path=file.path(outFigPath, "svg"))
unlink("PtsABdHiAnnoyAbsVarsConPermimpPtv.svg")
ggsave(filename="PtsABdHiAnnoyAbsVarsConPermimpPtv.pdf", width=8, height=10, path=file.path(outFigPath, "pdf"))
unlink("PtsABdHiAnnoyAbsVarsConPermimpPtv.pdf")
}
# Plot only values within 1% of the maximum
resultsOutAbs.conimp1pc <- resultsOutAbs.conimp |>
rownames_to_column('Metric') |>
filter_if(is.numeric, all_vars(. > max(resultsOutAbs.conimp)/100)) |>
column_to_rownames('Metric')
pBar <- ggplot(resultsOutAbs.conimp1pc,) + geom_col(aes(x=factor(rownames(resultsOutAbs.conimp1pc), levels=rownames(resultsOutAbs.conimp1pc)), y=CondPermImp), fill=mycolours[1], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar
if (saveplots){
ggsave(filename="PtsABdHiAnnoyAbsVarsConPermimp1pc.svg", width=8, height=4, path=file.path(outFigPath, "svg"))
unlink("PtsABdHiAnnoyAbsVarsConPermimp1pc.svg")
ggsave(filename="PtsABdHiAnnoyAbsVarsConPermimp1pc.pdf", width=8, height=4, path=file.path(outFigPath, "pdf"))
unlink("PtsABdHiAnnoyAbsVarsConPermimp1pc.pdf")
}
Selected metric
absVar <- "UASLoudECMAPowAvgBin"
SQM analysis
Individual SQMs
Sharpness
Set variables
iVars <- c(absVar, eventVar, ambVar, "UASSharpAurISO3PowAvgBin", "UASSharpAurISO305ExBin", "UASSharpAurSHMPowAvgBin", "UASSharpAurSHM05ExBin", "UASSharpAurISO1PowAvgBin", "UASSharpAurISO105ExBin", "UASSharpvBISO1PowAvgBin", "UASSharpvBISO105ExBin", "UASSharpDINPowAvgBin", "UASSharpDIN05ExBin", "UASSharpAurISO1MedBin",
"UASTonShpAurSHMPowAvgBin", "UASTonShpAurSHM05ExBin")
dVar <- "dHighAnnoyPc"
seeds <- c(7041, 905, 4984651, 6513213, 120651)
Hyperparameter tuning
p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)p
NA
NASelected hyperparameters
ntree <- 1501
mtry <- as.integer(length(iVars)/2.25)
Run model
Train preliminary model
nperm <- 5
resultsOutSharp <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutSharp$OOB_RMSE[1] 5.965448resultsOutSharp$OOB_MAE[1] 4.532257resultsOutSharp$Rsquared[1] 0.6957333Train multiple seeds model
resultsOutSharp <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutSharp$OOB_RMSE[1] 5.945878resultsOutSharp$OOB_MAE[1] 4.51012resultsOutSharp$Rsquared[1] 0.6984185
# store results
resdHiAnnoyFitAB['Abs sharp', 'RMSE'] <- resultsOutSharp$OOB_RMSE
resdHiAnnoyFitAB['Abs sharp', 'MAE'] <- resultsOutSharp$OOB_MAE
resdHiAnnoyFitAB['Abs sharp', 'Rsquared'] <- resultsOutSharp$Rsquared
resdHiAnnoyPermImpAB$AbsSharp <- resultsOutSharp$conditional_permimp
Plot results
par(mai=c(0,3,0,0))
# plot conditional importance
resultsOutSharp.conimp <- arrange(resultsOutSharp$conditional_permimp, desc(row_number()))
pBar <- ggplot(resultsOutSharp.conimp) + geom_col(aes(x=factor(rownames(resultsOutSharp.conimp), levels=rownames(resultsOutSharp.conimp)), y=CondPermImp), fill=mycolours[2], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + ggtitle("Sharpness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar
if (saveplots){
ggsave(filename="PtsABdHiAnnoySharpConPermimp.svg", width=8, height=4.9, path=file.path(outFigPath, "svg"))
unlink("PtsABdHiAnnoySharpConPermimp.svg")
ggsave(filename="PtsABdHiAnnoySharpConPermimp.pdf", width=8, height=4.9, path=file.path(outFigPath, "pdf"))
unlink("PtsABdHiAnnoySharpConPermimp.pdf")
}
Selected metric
sharpVar <- "UASSharpAurISO3PowAvgBin"
Tonal loudness and tonality
Set variables
iVars <- c(absVar, eventVar, ambVar, "UASTonalECMAAvgMaxLR", "UASTonalSHMInt05ExMaxLR", "UASTonalSHMIntAvgMaxLR", "UASTonalECMA05ExMaxLR", "UASTonalAwSHMAvgMaxLR", "UASTonalAwSHM05ExMaxLR", "UASTonalAwSHMIntAvgMaxLR", "UASTonalAwSHMInt05ExMaxLR", "UASTonLdECMAPowAvgBin", "UASTonLdECMA05ExBin", "UASTonalAurAvgMaxLR", "UASTonalAur05ExMaxLR", "UASTonalAur10ExMaxLR",
"UASTonShpAurSHMPowAvgBin", "UASTonShpAurSHM05ExBin")
dVar <- "dHighAnnoyPc"
seeds <- c(540, 104798, 456464, 87331, 94564)
Hyperparameter tuning
p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)p
Selected hyperparameters
ntree <- 251
mtry <- as.integer(length(iVars)/1.5)
Run model
Train preliminary model
# Tonality with tonal loudness
nperm <- 5
resultsOutTonal1 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutTonal1$OOB_RMSE[1] 6.358715resultsOutTonal1$OOB_MAE[1] 4.808657resultsOutTonal1$Rsquared[1] 0.6484513Train multiple seeds model
# Tonality with tonal loudness
resultsOutTonal1 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutTonal1$OOB_RMSE[1] 6.320428resultsOutTonal1$OOB_MAE[1] 4.782126resultsOutTonal1$Rsquared[1] 0.6527379# store results
resdHiAnnoyFitAB['Abs tonal inc loud', 'RMSE'] <- resultsOutTonal1$OOB_RMSE
resdHiAnnoyFitAB['Abs tonal inc loud', 'MAE'] <- resultsOutTonal1$OOB_MAE
resdHiAnnoyFitAB['Abs tonal inc loud', 'Rsquared'] <- resultsOutTonal1$Rsquared
resdHiAnnoyPermImpAB$AbsTonal1 <- resultsOutTonal1$conditional_permimp
Plot results
par(mai=c(0,3,0,0))
# plot conditional importance
resultsOutTonal1.conimp <- arrange(resultsOutTonal1$conditional_permimp, desc(row_number()))
pBar <- ggplot(resultsOutTonal1.conimp) + geom_col(aes(x=factor(rownames(resultsOutTonal1.conimp), levels=rownames(resultsOutTonal1.conimp)), y=CondPermImp), fill=mycolours[3], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + ggtitle("Tonality inc. tonal loudness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 110))
pBar
if (saveplots){
ggsave(filename="PtsABdHiAnnoyTonalLdConPermimp.svg", width=8, height=4.4, path=file.path(outFigPath, "svg"))
unlink("PtsABdHiAnnoyTonalLdConPermimp.svg")
ggsave(filename="PtsABdHiAnnoyTonalLdConPermimp.pdf", width=8, height=4.4, path=file.path(outFigPath, "pdf"))
unlink("PtsABdHiAnnoyTonalLdConPermimp.pdf")
}
Selected metric
tonLdVar <- "UASTonLdECMAPowAvgBin"
Tonality without tonal loudness
Set variables
iVars <- c(absVar, eventVar, ambVar, "UASTonalECMAAvgMaxLR", "UASTonalSHMInt05ExMaxLR", "UASTonalSHMIntAvgMaxLR", "UASTonalECMA05ExMaxLR", "UASTonalAwSHMAvgMaxLR", "UASTonalAwSHM05ExMaxLR", "UASTonalAwSHMIntAvgMaxLR", "UASTonalAwSHMInt05ExMaxLR", "UASTonalAurAvgMaxLR", "UASTonalAur05ExMaxLR", "UASTonalAur10ExMaxLR")
dVar <- "dHighAnnoyPc"
seeds <- c(156089, 5860, 10528, 89541, 4685146)
Hyperparameter tuning
p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)p
Selected hyperparameters
ntree <- 251
mtry <- as.integer(length(iVars)/1.25)
Run model
Train preliminary model
# Tonality
nperm <- 5
resultsOutTonal2 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm,
minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutTonal2$OOB_RMSE[1] 6.434733resultsOutTonal2$OOB_MAE[1] 4.888113resultsOutTonal2$Rsquared[1] 0.640617Train multiple seeds model
# Tonality
resultsOutTonal2 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutTonal2$OOB_RMSE[1] 6.454118resultsOutTonal2$OOB_MAE[1] 4.898967resultsOutTonal2$Rsquared[1] 0.6386236
# store results
resdHiAnnoyFitAB['Abs tonal no loud', 'RMSE'] <- resultsOutTonal2$OOB_RMSE
resdHiAnnoyFitAB['Abs tonal no loud', 'MAE'] <- resultsOutTonal2$OOB_MAE
resdHiAnnoyFitAB['Abs tonal no loud', 'Rsquared'] <- resultsOutTonal2$Rsquared
resdHiAnnoyPermImpAB$AbsTonal2 <- resultsOutTonal2$conditional_permimp
Plot results
par(mai=c(0,3,0,0))
# plot conditional importance
resultsOutTonal2.conimp <- arrange(resultsOutTonal2$conditional_permimp, desc(row_number()))
pBar <- ggplot(resultsOutTonal2.conimp) + geom_col(aes(x=factor(rownames(resultsOutTonal2.conimp), levels=rownames(resultsOutTonal2.conimp)), y=CondPermImp), fill=mycolours[3], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + ggtitle("Tonality w/o tonal loudness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 110))
pBar
if (saveplots){
ggsave(filename="PtsABdHiAnnoyTonalConPermimp.svg", width=8, height=3.8, path=file.path(outFigPath, "svg"))
unlink("PtsABdHiAnnoyTonalConPermimp.svg")
ggsave(filename="PtsABdHiAnnoyTonalConPermimp.pdf", width=8, height=3.8, path=file.path(outFigPath, "pdf"))
unlink("PtsABdHiAnnoyTonalConPermimp.pdf")
}
Selected metric
tonalVar <- "UASTonalAwSHMInt05ExMaxLR"
Fluctuation strength
Set variables
# Fluctuation strength
iVars <- c(absVar, eventVar, ambVar, "UASFluctOldSHM10ExBin", "UASFluctOldSHM05ExBin", "UASFluctECMA10ExBin", "UASFluctECMA05ExBin", "UASFluctFZ10ExMaxLR", "UASFluctFZ05ExMaxLR", "UASFluctOV10ExMaxLR", "UASFluctOV05ExMaxLR")
dVar <- "dHighAnnoyPc"
seeds <- c(25107, 546098, 195, 5937, 102658)
Hyperparameter tuning
p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)p
Selected hyperparameters
ntree <- 251
mtry <- as.integer(length(iVars)/1.25)
Run model
Train preliminary model
nperm <- 5
resultsOutFluct <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres,
nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutFluct$OOB_RMSE[1] 6.468882resultsOutFluct$OOB_MAE[1] 4.835973resultsOutFluct$Rsquared[1] 0.636221Train multiple seeds model
resultsOutFluct <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutFluct$OOB_RMSE[1] 6.48319resultsOutFluct$OOB_MAE[1] 4.846364resultsOutFluct$Rsquared[1] 0.6345453
# store results
resdHiAnnoyFitAB['Abs fluct', 'RMSE'] <- resultsOutFluct$OOB_RMSE
resdHiAnnoyFitAB['Abs fluct', 'MAE'] <- resultsOutFluct$OOB_MAE
resdHiAnnoyFitAB['Abs fluct', 'Rsquared'] <- resultsOutFluct$Rsquared
resdHiAnnoyPermImpAB$AbsFluct <- resultsOutFluct$conditional_permimp
Plot results
par(mai=c(0,3,0,0))
# plot conditional importance
resultsOutFluct.conimp <- arrange(resultsOutFluct$conditional_permimp, desc(row_number()))
pBar <- ggplot(resultsOutFluct.conimp) + geom_col(aes(x=factor(rownames(resultsOutFluct.conimp), levels=rownames(resultsOutFluct.conimp)), y=CondPermImp), fill=mycolours[4], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + ggtitle("Fluctuation strength") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar
if (saveplots){
ggsave(filename="PtsABdHiAnnoyFluctConPermimp.svg", width=8, height=2.9, path=file.path(outFigPath, "svg"))
unlink("PtsABdHiAnnoyFluctConPermimp.svg")
ggsave(filename="PtsABdHiAnnoyFluctConPermimp.pdf", width=8, height=2.9, path=file.path(outFigPath, "pdf"))
unlink("PtsABdHiAnnoyFluctConPermimp.pdf")
}
Selected metric
fluctVar <- "UASFluctECMA10ExBin"
Roughness
Set variables
# Roughness
iVars <- c(absVar, eventVar, ambVar, "UASRoughECMA10ExBin", "UASRoughECMA05ExBin", "UASRoughFZ10ExMaxLR", "UASRoughFZ05ExMaxLR", "UASRoughDW10ExMaxLR", "UASRoughDW05ExMaxLR")
dVar <- "dHighAnnoyPc"
seeds <- c(4701, 52187, 16589, 65217, 16893)
Hyperparameter tuning
p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)p
Selected hyperparameters
ntree <- 4001
mtry <- as.integer(length(iVars)/1.5)
Run model
Train preliminary model
nperm <- 5
resultsOutRough <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutRough$OOB_RMSE[1] 6.370114resultsOutRough$OOB_MAE[1] 4.823308resultsOutRough$Rsquared[1] 0.6515551Train multiple seeds model
resultsOutRough <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutRough$OOB_RMSE[1] 6.398763resultsOutRough$OOB_MAE[1] 4.841106resultsOutRough$Rsquared[1] 0.6481992# store results
resdHiAnnoyFitAB['Abs rough', 'RMSE'] <- resultsOutRough$OOB_RMSE
resdHiAnnoyFitAB['Abs rough', 'MAE'] <- resultsOutRough$OOB_MAE
resdHiAnnoyFitAB['Abs rough', 'Rsquared'] <- resultsOutRough$Rsquared
resdHiAnnoyPermImpAB$AbsRough <- resultsOutRough$conditional_permimp
Plot results
par(mai=c(0,3,0,0))
# plot conditional importance
resultsOutRough.conimp <- arrange(resultsOutRough$conditional_permimp, desc(row_number()))
pBar <- ggplot(resultsOutRough.conimp) + geom_col(aes(x=factor(rownames(resultsOutRough.conimp), levels=rownames(resultsOutRough.conimp)), y=CondPermImp), fill=mycolours[5], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + ggtitle("Roughness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar
if (saveplots){
ggsave(filename="PtsABdHiAnnoyRoughConPermimp.svg", width=8, height=2.9, path=file.path(outFigPath, "svg"))
unlink("PtsABdHiAnnoyRoughConPermimp.svg")
ggsave(filename="PtsABdHiAnnoyRoughConPermimp.pdf", width=8, height=2.9, path=file.path(outFigPath, "pdf"))
unlink("PtsABdHiAnnoyRoughConPermimp.pdf")
}
Selected metric
roughVar <- "UASRoughFZ05ExMaxLR"
Impulsiveness
Set variables
# Impulsiveness
iVars <- c(absVar, eventVar, ambVar, "UASImpulsSHMAvgMaxLR", "UASImpulsSHM05ExMaxLR", "UASImpulsSHMPowAvgMaxLR", "UASImpulsLoudWZAvgMaxLR", "UASImpulsLoudWZ05ExMaxLR", "UASImpulsLoudWZPowAvgMaxLR", "UASImpulsLoudWECMAAvgBin", "UASImpulsLoudWECMA05ExBin", "UASImpulsLoudWECMAPowAvgBin")
dVar <- "dHighAnnoyPc"
seeds <- c(8495, 59867, 5416, 9843, 86)
Hyperparameter tuning
p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)p
Selected hyperparameters
ntree <- 501
mtry <- as.integer(length(iVars)/1.5)
Run model
Train preliminary model
nperm <- 5
resultsOutImpuls <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutImpuls$OOB_RMSE[1] 6.270202resultsOutImpuls$OOB_MAE[1] 4.842259resultsOutImpuls$Rsquared[1] 0.6582782Train multiple seeds model
resultsOutImpuls <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutImpuls$OOB_RMSE[1] 6.270233resultsOutImpuls$OOB_MAE[1] 4.84118resultsOutImpuls$Rsquared[1] 0.6582749
# store results
resdHiAnnoyFitAB['Abs impuls', 'RMSE'] <- resultsOutImpuls$OOB_RMSE
resdHiAnnoyFitAB['Abs impuls', 'MAE'] <- resultsOutImpuls$OOB_MAE
resdHiAnnoyFitAB['Abs impuls', 'Rsquared'] <- resultsOutImpuls$Rsquared
resdHiAnnoyPermImpAB$AbsImpuls <- resultsOutImpuls$conditional_permimp
Plot results
par(mai=c(0,3,0,0))
# plot conditional importance
resultsOutImpuls.conimp <- arrange(resultsOutImpuls$conditional_permimp, desc(row_number()))
pBar <- ggplot(resultsOutImpuls.conimp) + geom_col(aes(x=factor(rownames(resultsOutImpuls.conimp), levels=rownames(resultsOutImpuls.conimp)), y=CondPermImp), fill=mycolours[6], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + ggtitle("Impulsiveness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar
if (saveplots){
ggsave(filename="PtsABdHiAnnoyImpulsConPermimp.svg", width=8, height=4, path=file.path(outFigPath, "svg"))
unlink("PtsABdHiAnnoyImpulsConPermimp.svg")
ggsave(filename="PtsABdHiAnnoyImpulsConPermimp.pdf", width=8, height=4, path=file.path(outFigPath, "pdf"))
unlink("PtsABdHiAnnoyImpulsConPermimp.pdf")
}
Selected metric
impulsVar <- "UASImpulsLoudWZAvgMaxLR"
SQM and loudness comparison
Now the highest importance SQMs are ranked against each other, controlling for UAS loudness and ambient LAeq.
Include tonal loudness
Set variables
iVars <- c(absVar, eventVar, ambVar, sharpVar, tonLdVar, fluctVar, roughVar, impulsVar)
dVar <- "dHighAnnoyPc"
seeds <- c(70498, 4, 14986, 453, 864)
Hyperparameter tuning
p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)p
Selected hyperparameters
ntree <- 1501
mtry <- 3
Run model
Train preliminary model
nperm <- 5
resultsOutSQMs1 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutSQMs1$OOB_RMSE[1] 6.112539resultsOutSQMs1$OOB_MAE[1] 4.697572resultsOutSQMs1$Rsquared[1] 0.6758278Train multiple seeds model
resultsOutSQMs1 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutSQMs1$OOB_RMSE[1] 6.115048resultsOutSQMs1$OOB_MAE[1] 4.695767resultsOutSQMs1$Rsquared[1] 0.6754898
# store results
resdHiAnnoyFitAB['Abs SQMs inc tonal loud', 'RMSE'] <- resultsOutSQMs1$OOB_RMSE
resdHiAnnoyFitAB['Abs SQMs inc tonal loud', 'MAE'] <- resultsOutSQMs1$OOB_MAE
resdHiAnnoyFitAB['Abs SQMs inc tonal loud', 'Rsquared'] <- resultsOutSQMs1$Rsquared
resdHiAnnoyPermImpAB$AbsSQMs1 <- resultsOutSQMs1$conditional_permimp
Plot results
par(mai=c(0,3,0,0))
# plot conditional importance
resultsOutSQMs1.conimp <- arrange(resultsOutSQMs1$conditional_permimp, desc(row_number()))
pBar <- ggplot(resultsOutSQMs1.conimp) + geom_col(aes(x=factor(rownames(resultsOutSQMs1.conimp), levels=rownames(resultsOutSQMs1.conimp)), y=CondPermImp), fill=mycolours[7], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 30))
pBar
if (saveplots){
ggsave(filename="PtsABdHiAnnoyAbsSQMsTonLdConPermimp.svg", width=8, height=2.4, path=file.path(outFigPath, "svg"))
unlink("PtsABdHiAnnoyAbsSQMsTonLdConPermimp.svg")
ggsave(filename="PtsABdHiAnnoyAbsSQMsTonLdConPermimp.pdf", width=8, height=2.4, path=file.path(outFigPath, "pdf"))
unlink("PtsABdHiAnnoyAbsSQMsTonLdConPermimp.pdf")
}
Exclude tonal loudness
Set variables
iVars <- c(absVar, eventVar, ambVar, sharpVar, tonalVar, fluctVar, roughVar, impulsVar)
dVar <- "dHighAnnoyPc"
seeds <- c(546, 57203, 270835, 60592, 8094)
Hyperparameter tuning
p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)p
Selected hyperparameters
ntree <- 4001
mtry <- 3
Run model
Train preliminary model
nperm <- 5
resultsOutSQMs2 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutSQMs2$OOB_RMSE[1] 6.081512resultsOutSQMs2$OOB_MAE[1] 4.686507resultsOutSQMs2$Rsquared[1] 0.6810178Train multiple seeds model
resultsOutSQMs2 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutSQMs2$OOB_RMSE[1] 6.101152resultsOutSQMs2$OOB_MAE[1] 4.702343resultsOutSQMs2$Rsquared[1] 0.6787822
# store results
resdHiAnnoyFitAB['Abs SQMs no tonal loud', 'RMSE'] <- resultsOutSQMs2$OOB_RMSE
resdHiAnnoyFitAB['Abs SQMs no tonal loud', 'MAE'] <- resultsOutSQMs2$OOB_MAE
resdHiAnnoyFitAB['Abs SQMs no tonal loud', 'Rsquared'] <- resultsOutSQMs2$Rsquared
resdHiAnnoyPermImpAB$AbsSQMs2 <- resultsOutSQMs2$conditional_permimp
Plot results
par(mai=c(0,3,0,0))
# plot conditional importance
resultsOutSQMs2.conimp <- arrange(resultsOutSQMs2$conditional_permimp, desc(row_number()))
pBar <- ggplot(resultsOutSQMs2.conimp) + geom_col(aes(x=factor(rownames(resultsOutSQMs2.conimp), levels=rownames(resultsOutSQMs2.conimp)), y=CondPermImp), fill=mycolours[7], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 30))
pBar
if (saveplots){
ggsave(filename="PtsABdHiAnnoyAbsSQMsNoTonLdConPermimp.svg", width=8, height=2.4, path=file.path(outFigPath, "svg"))
unlink("PtsABdHiAnnoyAbsSQMsNoTonLdConPermimp.svg")
ggsave(filename="PtsABdHiAnnoyAbsSQMsNoTonLdConPermimp.pdf", width=8, height=2.4, path=file.path(outFigPath, "pdf"))
unlink("PtsABdHiAnnoyAbsSQMsNoTonLdConPermimp.pdf")
}
Psychoacoustic annoyance metrics
Set variables
iVars <- c(ambVar, "UASPsychAnnoyWidmann", "UASPsychAnnoyMore", "UASPsychAnnoyDi", "UASPsychAnnoyTorija", "UASPsychAnnoyWillemsen", "UASPsychAnnoyBoucher")
dVar <- "dHighAnnoyPc"
seeds <- c(48651, 45, 785123, 65, 5163)
Hyperparameter tuning
p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)p
Selected hyperparameters
ntree <- 4001
mtry <- 4
Run model
Train preliminary model
nperm <- 5
resultsOutPA <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutPA$OOB_RMSE[1] 6.88072resultsOutPA$OOB_MAE[1] 5.141535resultsOutPA$Rsquared[1] 0.5875095Train multiple seeds model
resultsOutPA <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutPA$OOB_RMSE[1] 6.879401resultsOutPA$OOB_MAE[1] 5.138723resultsOutPA$Rsquared[1] 0.5876636
# store results
resdHiAnnoyFitAB['Psychoacoustic annoyance', 'RMSE'] <- resultsOutPA$OOB_RMSE
resdHiAnnoyFitAB['Psychoacoustic annoyance', 'MAE'] <- resultsOutPA$OOB_MAE
resdHiAnnoyFitAB['Psychoacoustic annoyance', 'Rsquared'] <- resultsOutPA$Rsquared
resdHiAnnoyPermImpAB$AbsPA <- resultsOutPA$conditional_permimp
Plot results
par(mai=c(0,3,0,0))
# plot conditional importance
resultsOutPA.conimp <- arrange(resultsOutPA$conditional_permimp, desc(row_number()))
pBar <- ggplot(resultsOutPA.conimp) + geom_col(aes(x=factor(rownames(resultsOutPA.conimp), levels=rownames(resultsOutPA.conimp)), y=CondPermImp), fill=mycolours[10], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 60))
pBar
if (saveplots){
ggsave(filename="PtsABdHiAnnoyAbsPAConPermimp.svg", width=8, height=2.4, path=file.path(outFigPath, "svg"))
unlink("PtsABdHiAnnoyAbsPAConPermimp.svg")
ggsave(filename="PtsABdHiAnnoyAbsPAConPermimp.pdf", width=8, height=2.4, path=file.path(outFigPath, "pdf"))
unlink("PtsABdHiAnnoyAbsPAConPermimp.pdf")
}
All variables (absolute and difference)
Set variables
iVars <- names(stimDataNum)[which(names(stimDataNum) == 'UASEvents'):which(names(stimDataNum) == 'UASPsychAnnoyBoucher')]
iVars <- iVars[! iVars %in% 'SNRlevel']
iVars <- c(iVars,
names(stimDataNum)[which(colnames(stimDataNum)=='LAeqLAF90diff'):
which(colnames(stimDataNum)=='dPsychAnnoyBoucher')], 'SNRlevel')
dVar <- "dHighAnnoyPc"
seeds <- c(2, 312, 1897, 465978, 821659)
Hyperparameter tuning
p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)p
Selected hyperparameters
ntree <- 1501
mtry <- as.integer(length(iVars)/3.5)
Run model
Train preliminary model
nperm <- 5
resultsOutAbsDiffs <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutAbsDiffs$OOB_RMSE[1] 6.145585resultsOutAbsDiffs$OOB_MAE[1] 4.687502resultsOutAbsDiffs$Rsquared[1] 0.6754223Train multiple seeds model
resultsOutAbsDiffs <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutAbsDiffs$OOB_RMSE[1] 6.145665resultsOutAbsDiffs$OOB_MAE[1] 4.694338resultsOutAbsDiffs$Rsquared[1] 0.676116# store results
resdHiAnnoyFitAB['All vars', 'RMSE'] <- resultsOutAbsDiffs$OOB_RMSE
resdHiAnnoyFitAB['All vars', 'MAE'] <- resultsOutAbsDiffs$OOB_MAE
resdHiAnnoyFitAB['All vars', 'Rsquared'] <- resultsOutAbsDiffs$Rsquared
resdHiAnnoyPermImpAB$AllVars <- resultsOutAbsDiffs$conditional_permimp
Plot results
par(mai=c(0,3,0,0))
# plot conditional importance
resultsOutAbsDiffs.conimp <- arrange(resultsOutAbsDiffs$conditional_permimp, desc(row_number()))
pBar <- ggplot(resultsOutAbsDiffs.conimp) + geom_col(aes(x=factor(rownames(resultsOutAbsDiffs.conimp), levels=rownames(resultsOutAbsDiffs.conimp)), y=CondPermImp), fill=mycolours[9], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar
if (saveplots){
ggsave(filename="PtsABdHiAnnoyAllVarsConPermimp.svg", width=8, height=26, path=file.path(outFigPath, "svg"))
unlink("PtsABdHiAnnoyAllVarsConPermimp.svg")
ggsave(filename="PtsABdHiAnnoyAllVarsConPermimp.pdf", width=8, height=26, path=file.path(outFigPath, "pdf"))
unlink("PtsABdHiAnnoyAllVarsConPermimp.pdf")
}
# Plot only positive values
resultsOutAbsDiffs.conimpPtv <- resultsOutAbsDiffs.conimp |>
rownames_to_column('Metric') |>
filter_if(is.numeric, all_vars(. > 0)) |>
column_to_rownames('Metric')
pBar <- ggplot(resultsOutAbsDiffs.conimpPtv) + geom_col(aes(x=factor(rownames(resultsOutAbsDiffs.conimpPtv), levels=rownames(resultsOutAbsDiffs.conimpPtv)), y=CondPermImp), fill=mycolours[9], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar
if (saveplots){
ggsave(filename="PtsABdHiAnnoyAllVarsConPermimpPtv.svg", width=8, height=22, path=file.path(outFigPath, "svg"))
unlink("PtsABdHiAnnoyAllVarsConPermimp.svg")
ggsave(filename="PtsABdHiAnnoyAllVarsConPermimpPtv.pdf", width=8, height=22, path=file.path(outFigPath, "pdf"))
unlink("PtsABdHiAnnoyAllVarsConPermimp.pdf")
}
# Plot only values within 1% of the maximum
resultsOutAbsDiffs.conimp1pc <- resultsOutAbsDiffs.conimp |>
rownames_to_column('Metric') |>
filter_if(is.numeric, all_vars(. > max(resultsOutAbsDiffs.conimp)/100)) |>
column_to_rownames('Metric')
pBar <- ggplot(resultsOutAbsDiffs.conimp1pc) + geom_col(aes(x=factor(rownames(resultsOutAbsDiffs.conimp1pc), levels=rownames(resultsOutAbsDiffs.conimp1pc)), y=CondPermImp), fill=mycolours[9], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar
if (saveplots){
ggsave(filename="PtsABdHiAnnoyAllVarsConPermimp1pc.svg", width=8, height=7, path=file.path(outFigPath, "svg"))
unlink("PtsABdHiAnnoyAllVarsConPermimp1pc.svg")
ggsave(filename="PtsABdHiAnnoyAllVarsConPermimp1pc.pdf", width=8, height=7, path=file.path(outFigPath, "pdf"))
unlink("PtsABdHiAnnoyAllVarsConPermimp1pc.pdf")
}
Selected metric
allVar <- "UASLoudECMAPowAvgBin"
dSQM analysis
Individual SQMs
dSharpness
Set variables
iVars <- c(allVar, eventVar, ambVar, "dSharpAurISO3PowAvgBin", "dSharpAurISO305ExBin", "dSharpAurSHMPowAvgBin", "dSharpAurSHM05ExBin", "dTonShpAurSHMPowAvgBin", "dTonShpAurSHM05ExBin", "PartTonShpAurSHMPowAvgBin",
"PartTonShpAurSHM05ExBin", "UASSharpAurISO3PowAvgBin", "UASSharpAurISO305ExBin", "UASSharpAurSHMPowAvgBin", "UASSharpAurSHM05ExBin", "UASSharpAurISO1PowAvgBin", "UASSharpAurISO105ExBin", "UASSharpvBISO1PowAvgBin", "UASSharpvBISO105ExBin", "UASSharpDINPowAvgBin", "UASSharpDIN05ExBin", "UASSharpAurISO1MedBin",
"UASTonShpAurSHMPowAvgBin", "UASTonShpAurSHM05ExBin")
dVar <- "dHighAnnoyPc"
seeds <- c(84194, 905, 64815, 928054, 625091, 582031)
Hyperparameter tuning
p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)p
Selected hyperparameters
ntree <- 251
mtry <- as.integer(length(iVars)/2.25)
Run model
Train preliminary model
nperm <- 10
resultsOutSharp <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutSharp$OOB_RMSE[1] 6.03978resultsOutSharp$OOB_MAE[1] 4.513288resultsOutSharp$Rsquared[1] 0.6875739Train multiple seeds model
resultsOutSharp <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutSharp$OOB_RMSE[1] 6.105942resultsOutSharp$OOB_MAE[1] 4.555393resultsOutSharp$Rsquared[1] 0.6794885# store results
resdHiAnnoyFitAB['All sharp', 'RMSE'] <- resultsOutSharp$OOB_RMSE
resdHiAnnoyFitAB['All sharp', 'MAE'] <- resultsOutSharp$OOB_MAE
resdHiAnnoyFitAB['All sharp', 'Rsquared'] <- resultsOutSharp$Rsquared
resdHiAnnoyPermImpAB$AllSharp <- resultsOutSharp$conditional_permimp
Plot results
par(mai=c(0,3,0,0))
# plot conditional importance
resultsOutSharp.conimp <- arrange(resultsOutSharp$conditional_permimp, desc(row_number()))
pBar <- ggplot(resultsOutSharp.conimp) + geom_col(aes(x=factor(rownames(resultsOutSharp.conimp), levels=rownames(resultsOutSharp.conimp)), y=CondPermImp), fill=mycolours[2], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + ggtitle("All sharpness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar
if (saveplots){
ggsave(filename="PtsABdHiAnnoyAllSharpConPermimp.svg", width=8, height=5, path=file.path(outFigPath, "svg"))
unlink("PtsABdHiAnnoyAllSharpConPermimp.svg")
ggsave(filename="PtsABdHiAnnoyAllSharpConPermimp.pdf", width=8, height=5, path=file.path(outFigPath, "pdf"))
unlink("PtsABdHiAnnoyAllSharpConPermimp.pdf")
}
Selected metric
allSharpVar <- "dSharpAurSHMPowAvgBin"
dTonal loudness and dtonality
Set variables
iVars <- c(allVar, eventVar, ambVar, "dTonalECMAAvgMaxLR", "dTonalSHMInt05ExMaxLR", "dTonalSHMIntAvgMaxLR", "dTonalECMA05ExMaxLR", "dTonalAwSHMAvgMaxLR", "dTonalAwSHM05ExMaxLR", "dTonalAwSHMIntAvgMaxLR", "dTonalAwSHMInt05ExMaxLR", "dTonLdECMAPowAvgBin", "dTonLdECMA05ExBin", "dTonShpAurSHMPowAvgBin",
"dTonShpAurSHM05ExBin", "PartTonLdSHMPowAvgBin", "UASTonalECMAAvgMaxLR", "UASTonalSHMInt05ExMaxLR", "UASTonalSHMIntAvgMaxLR", "UASTonalECMA05ExMaxLR", "UASTonalAwSHMAvgMaxLR", "UASTonalAwSHM05ExMaxLR", "UASTonalAwSHMIntAvgMaxLR", "UASTonalAwSHMInt05ExMaxLR", "UASTonLdECMAPowAvgBin", "UASTonLdECMA05ExBin", "UASTonalAurAvgMaxLR", "UASTonalAur05ExMaxLR", "UASTonalAur10ExMaxLR",
"UASTonShpAurSHMPowAvgBin", "UASTonShpAurSHM05ExBin")
dVar <- "dHighAnnoyPc"
Hyperparameter tuning
p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)p
seeds <- c(561684, 104798, 1536, 48, 48561)
Selected hyperparameters
ntree <- 501
mtry <- as.integer(length(iVars)/2.25)
Run model
Train preliminary model
# Tonality with tonal loudness
nperm <- 5
resultsOutTonal1 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutTonal1$OOB_RMSE[1] 6.240305resultsOutTonal1$OOB_MAE[1] 4.837859resultsOutTonal1$Rsquared[1] 0.6655154Train multiple seeds model
# Tonality with tonal loudness
resultsOutTonal1 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutTonal1$OOB_RMSE[1] 6.300441resultsOutTonal1$OOB_MAE[1] 4.883414resultsOutTonal1$Rsquared[1] 0.6581737# store results
resdHiAnnoyFitAB['All tonal inc loud', 'RMSE'] <- resultsOutTonal1$OOB_RMSE
resdHiAnnoyFitAB['All tonal inc loud', 'MAE'] <- resultsOutTonal1$OOB_MAE
resdHiAnnoyFitAB['All tonal inc loud', 'Rsquared'] <- resultsOutTonal1$Rsquared
resdHiAnnoyPermImpAB$AllTonal1 <- resultsOutTonal1$conditional_permimp
Plot results
par(mai=c(0,3,0,0))
# plot conditional importance
resultsOutTonal1.conimp <- arrange(resultsOutTonal1$conditional_permimp, desc(row_number()))
pBar <- ggplot(resultsOutTonal1.conimp) + geom_col(aes(x=factor(rownames(resultsOutTonal1.conimp), levels=rownames(resultsOutTonal1.conimp)), y=CondPermImp), fill=mycolours[3], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + ggtitle("All tonality inc. tonal loudness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 50))
pBar
if (saveplots){
ggsave(filename="PtsABdHiAnnoyAllTonalLdConPermimp.svg", width=8, height=6, path=file.path(outFigPath, "svg"))
unlink("PtsABdHiAnnoyAllTonalLdConPermimp.svg")
ggsave(filename="PtsABdHiAnnoyAllTonalLdConPermimp.pdf", width=8, height=6, path=file.path(outFigPath, "pdf"))
unlink("PtsABdHiAnnoyAllTonalLdConPermimp.pdf")
}
Selected metric
allTonLdVar <- "UASTonLdECMAPowAvgBin"
dTonality without dtonal loudness
Set variables
iVars <- c(allVar, eventVar, ambVar, "dTonalECMAAvgMaxLR", "dTonalSHMInt05ExMaxLR", "dTonalSHMIntAvgMaxLR", "dTonalECMA05ExMaxLR", "dTonalAwSHMAvgMaxLR", "dTonalAwSHM05ExMaxLR", "dTonalAwSHMIntAvgMaxLR", "dTonalAwSHMInt05ExMaxLR", "UASTonalECMAAvgMaxLR", "UASTonalSHMInt05ExMaxLR", "UASTonalSHMIntAvgMaxLR", "UASTonalECMA05ExMaxLR", "UASTonalAwSHMAvgMaxLR", "UASTonalAwSHM05ExMaxLR", "UASTonalAwSHMIntAvgMaxLR", "UASTonalAwSHMInt05ExMaxLR", "UASTonalAurAvgMaxLR", "UASTonalAur05ExMaxLR", "UASTonalAur10ExMaxLR")
dVar <- "dHighAnnoyPc"
seeds <- c(410865, 2954, 70812, 203, 7984)
Hyperparameter tuning
p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)p
Selected hyperparameters
ntree <- 501
mtry <- as.integer(length(iVars)/1.25)
Run model
Train preliminary model
# Tonality
nperm <- 5
resultsOutTonal2 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutTonal2$OOB_RMSE[1] 6.399904resultsOutTonal2$OOB_MAE[1] 4.887571resultsOutTonal2$Rsquared[1] 0.6451589Train multiple seeds model
# Tonality
resultsOutTonal2 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutTonal2$OOB_RMSE[1] 6.393998resultsOutTonal2$OOB_MAE[1] 4.900818resultsOutTonal2$Rsquared[1] 0.6463381
# store results
resdHiAnnoyFitAB['All tonal no loud', 'RMSE'] <- resultsOutTonal2$OOB_RMSE
resdHiAnnoyFitAB['All tonal no loud', 'MAE'] <- resultsOutTonal2$OOB_MAE
resdHiAnnoyFitAB['All tonal no loud', 'Rsquared'] <- resultsOutTonal2$Rsquared
resdHiAnnoyPermImpAB$AllTonal2 <- resultsOutTonal2$conditional_permimp
Plot results
par(mai=c(0,3,0,0))
# plot conditional importance
resultsOutTonal2.conimp <- arrange(resultsOutTonal2$conditional_permimp, desc(row_number()))
pBar <- ggplot(resultsOutTonal2.conimp) + geom_col(aes(x=factor(rownames(resultsOutTonal2.conimp), levels=rownames(resultsOutTonal2.conimp)), y=CondPermImp), fill=mycolours[3], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + ggtitle("All tonality w/o tonal loudness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 100))
pBar
if (saveplots){
ggsave(filename="PtsABdHiAnnoyAllTonalConPermimp.svg", width=8, height=4.8, path=file.path(outFigPath, "svg"))
unlink("PtsABdHiAnnoyAllTonalConPermimp.svg")
ggsave(filename="PtsABdHiAnnoyAllTonalConPermimp.pdf", width=8, height=4.8, path=file.path(outFigPath, "pdf"))
unlink("PtsABdHiAnnoyAllTonalConPermimp.pdf")
}
Selected metric
allTonalVar <- "UASTonalAwSHMInt05ExMaxLR"
dFluctuation strength
Set variables
# Fluctuation strength
iVars <- c(allVar, eventVar, ambVar, "dFluctECMA10ExBin", "dFluctECMA05ExBin", "dFluctOV10ExMaxLR", "dFluctOV05ExMaxLR", "UASFluctOldSHM10ExBin", "UASFluctOldSHM05ExBin", "UASFluctECMA10ExBin", "UASFluctECMA05ExBin", "UASFluctFZ10ExMaxLR", "UASFluctFZ05ExMaxLR", "UASFluctOV10ExMaxLR", "UASFluctOV05ExMaxLR")
dVar <- "dHighAnnoyPc"
seeds <- c(418657, 84, 1630, 18659, 3687)
Hyperparameter tuning
p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)p
Selected hyperparameters
ntree <- 251
mtry <- as.integer(length(iVars)/1.25)
Run model
Train preliminary model
nperm <- 5
resultsOutFluct <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutFluct$OOB_RMSE[1] 6.439173resultsOutFluct$OOB_MAE[1] 4.789859resultsOutFluct$Rsquared[1] 0.6391434Train multiple seeds model
resultsOutFluct <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutFluct$OOB_RMSE[1] 6.4464resultsOutFluct$OOB_MAE[1] 4.815919resultsOutFluct$Rsquared[1] 0.6384767
# store results
resdHiAnnoyFitAB['All fluct', 'RMSE'] <- resultsOutFluct$OOB_RMSE
resdHiAnnoyFitAB['All fluct', 'MAE'] <- resultsOutFluct$OOB_MAE
resdHiAnnoyFitAB['All fluct', 'Rsquared'] <- resultsOutFluct$Rsquared
resdHiAnnoyPermImpAB$AllFluct <- resultsOutFluct$conditional_permimp
Plot results
par(mai=c(0,3,0,0))
# plot conditional importance
resultsOutFluct.conimp <- arrange(resultsOutFluct$conditional_permimp, desc(row_number()))
pBar <- ggplot(resultsOutFluct.conimp) + geom_col(aes(x=factor(rownames(resultsOutFluct.conimp), levels=rownames(resultsOutFluct.conimp)), y=CondPermImp), fill=mycolours[4], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + ggtitle("All fluctuation strength") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar
if (saveplots){
ggsave(filename="PtsABdHiAnnoyAllFluctConPermimp.svg", width=8, height=4, path=file.path(outFigPath, "svg"))
unlink("PtsABdHiAnnoyAllFluctConPermimp.svg")
ggsave(filename="PtsABdHiAnnoyAllFluctConPermimp.pdf", width=8, height=4, path=file.path(outFigPath, "pdf"))
unlink("PtsABdHiAnnoyAllFluctConPermimp.pdf")
}
Selected metric
allFluctVar <- "UASFluctECMA10ExBin"
dRoughness
Set variables
# Roughness
iVars <- c(allVar, eventVar, ambVar, "dRoughECMA10ExBin", "dRoughECMA05ExBin", "dRoughFZ10ExMaxLR", "dRoughFZ05ExMaxLR", "UASRoughECMA10ExBin", "UASRoughECMA05ExBin", "UASRoughFZ10ExMaxLR", "UASRoughFZ05ExMaxLR", "UASRoughDW10ExMaxLR", "UASRoughDW05ExMaxLR")
dVar <- "dHighAnnoyPc"
seeds <- c(69851, 85109, 410986, 1563, 896)
Hyperparameter tuning
p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)p
Selected hyperparameters
ntree <- 1501
mtry <- as.integer(length(iVars)/1.25)
Run model
Train preliminary model
nperm <- 5
resultsOutRough <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutRough$OOB_RMSE[1] 6.494263resultsOutRough$OOB_MAE[1] 4.835709resultsOutRough$Rsquared[1] 0.633995Train multiple seeds model
resultsOutRough <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutRough$OOB_RMSE[1] 6.495009resultsOutRough$OOB_MAE[1] 4.843281resultsOutRough$Rsquared[1] 0.6340358# store results
resdHiAnnoyFitAB['All rough', 'RMSE'] <- resultsOutRough$OOB_RMSE
resdHiAnnoyFitAB['All rough', 'MAE'] <- resultsOutRough$OOB_MAE
resdHiAnnoyFitAB['All rough', 'Rsquared'] <- resultsOutRough$Rsquared
resdHiAnnoyPermImpAB$AllRough <- resultsOutRough$conditional_permimp
Plot results
par(mai=c(0,3,0,0))
# plot conditional importance
resultsOutRough.conimp <- arrange(resultsOutRough$conditional_permimp, desc(row_number()))
pBar <- ggplot(resultsOutRough.conimp) + geom_col(aes(x=factor(rownames(resultsOutRough.conimp), levels=rownames(resultsOutRough.conimp)), y=CondPermImp), fill=mycolours[5], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + ggtitle("All roughness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar
if (saveplots){
ggsave(filename="PtsABdHiAnnoyAllRoughConPermimp.svg", width=8, height=4, path=file.path(outFigPath, "svg"))
unlink("PtsABdHiAnnoyAllRoughConPermimp.svg")
ggsave(filename="PtsABdHiAnnoyAllRoughConPermimp.pdf", width=8, height=4, path=file.path(outFigPath, "pdf"))
unlink("PtsABdHiAnnoyAllRoughConPermimp.pdf")
}
Selected metric
allRoughVar <- "dRoughFZ05ExMaxLR"
dImpulsiveness
Set variables
# Impulsiveness
iVars <- c(allVar, eventVar, ambVar, "dImpulsSHMAvgMaxLR", "dImpulsSHM05ExMaxLR", "dImpulsSHMPowAvgMaxLR",
"dImpulsLoudWZAvgMaxLR", "dImpulsLoudWZ05ExMaxLR", "dImpulsLoudWZPowAvgMaxLR",
"dImpulsLoudWECMAAvgBin", "dImpulsLoudWECMA05ExBin", "dImpulsLoudWECMAPowAvgBin", "UASImpulsSHMAvgMaxLR", "UASImpulsSHM05ExMaxLR", "UASImpulsSHMPowAvgMaxLR", "UASImpulsLoudWZAvgMaxLR", "UASImpulsLoudWZ05ExMaxLR", "UASImpulsLoudWZPowAvgMaxLR", "UASImpulsLoudWECMAAvgBin", "UASImpulsLoudWECMA05ExBin", "UASImpulsLoudWECMAPowAvgBin")
dVar <- "dHighAnnoyPc"
seeds <- c(418659, 7805, 38475, 65834, 1653)
Hyperparameter tuning
p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)p
Selected hyperparameters
ntree <- 5501
mtry <- as.integer(length(iVars)/1.25)
Run model
Train preliminary model
nperm <- 5
resultsOutImpuls <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutImpuls$OOB_RMSE[1] 6.462745resultsOutImpuls$OOB_MAE[1] 4.980449resultsOutImpuls$Rsquared[1] 0.6423632Train multiple seeds model
resultsOutImpuls <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutImpuls$OOB_RMSE[1] 6.478918resultsOutImpuls$OOB_MAE[1] 4.979735resultsOutImpuls$Rsquared[1] 0.6409678
# store results
resdHiAnnoyFitAB['All impuls', 'RMSE'] <- resultsOutImpuls$OOB_RMSE
resdHiAnnoyFitAB['All impuls', 'MAE'] <- resultsOutImpuls$OOB_MAE
resdHiAnnoyFitAB['All impuls', 'Rsquared'] <- resultsOutImpuls$Rsquared
resdHiAnnoyPermImpAB$AllImpuls <- resultsOutImpuls$conditional_permimp
Plot results
par(mai=c(0,3,0,0))
# plot conditional importance
resultsOutImpuls.conimp <- arrange(resultsOutImpuls$conditional_permimp, desc(row_number()))
pBar <- ggplot(resultsOutImpuls.conimp) + geom_col(aes(x=factor(rownames(resultsOutImpuls.conimp), levels=rownames(resultsOutImpuls.conimp)), y=CondPermImp), fill=mycolours[6], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + ggtitle("All impulsiveness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar
if (saveplots){
ggsave(filename="PtsABdHiAnnoyAllImpulsConPermimp.svg", width=8, height=5.6, path=file.path(outFigPath, "svg"))
unlink("PtsABdHiAnnoyAllImpulsConPermimp.svg")
ggsave(filename="PtsABdHiAnnoyAllImpulsConPermimp.pdf", width=8, height=5.6, path=file.path(outFigPath, "pdf"))
unlink("PtsABdHiAnnoyAllImpulsConPermimp.pdf")
}
Selected metric
allImpulsVar <- "UASImpulsLoudWZAvgMaxLR"
dSQM and loudness comparison
Now the highest importance dSQMs are ranked against each other, controlling for loudness difference.
Include dtonal loudness
Set variables
iVars <- c(allVar, eventVar, ambVar, allSharpVar, allTonLdVar, allFluctVar, allRoughVar, allImpulsVar)
dVar <- "dHighAnnoyPc"
seeds <- c(98465, 54163, 6541, 36485, 849675)
Hyperparameter tuning
p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)p
Selected hyperparameters
ntree <- 1001
mtry <- 3
Run model
Train preliminary model
nperm <- 5
resultsOutSQMs1 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutSQMs1$OOB_RMSE[1] 6.263055resultsOutSQMs1$OOB_MAE[1] 4.827668resultsOutSQMs1$Rsquared[1] 0.6595923Train multiple seeds model
resultsOutSQMs1 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutSQMs1$OOB_RMSE[1] 6.239049resultsOutSQMs1$OOB_MAE[1] 4.812234resultsOutSQMs1$Rsquared[1] 0.6620318
# store results
resdHiAnnoyFitAB['All SQMs inc tonal loud', 'RMSE'] <- resultsOutSQMs1$OOB_RMSE
resdHiAnnoyFitAB['All SQMs inc tonal loud', 'MAE'] <- resultsOutSQMs1$OOB_MAE
resdHiAnnoyFitAB['All SQMs inc tonal loud', 'Rsquared'] <- resultsOutSQMs1$Rsquared
resdHiAnnoyPermImpAB$AllSQMs1 <- resultsOutSQMs1$conditional_permimp
Plot results
par(mai=c(0,3,0,0))
# plot conditional importance
resultsOutSQMs1.conimp <- arrange(resultsOutSQMs1$conditional_permimp, desc(row_number()))
pBar <- ggplot(resultsOutSQMs1.conimp) + geom_col(aes(x=factor(rownames(resultsOutSQMs1.conimp), levels=rownames(resultsOutSQMs1.conimp)), y=CondPermImp), fill=mycolours[7], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 40))
pBar
if (saveplots){
ggsave(filename="PtsABdHiAnnoyAllSQMsTonLdConPermimp.svg", width=8, height=2.4, path=file.path(outFigPath, "svg"))
unlink("PtsABdHiAnnoyAllSQMsTonLdConPermimp.svg")
ggsave(filename="PtsABdHiAnnoyAllSQMsTonLdConPermimp.pdf", width=8, height=2.4, path=file.path(outFigPath, "pdf"))
unlink("PtsABdHiAnnoyAllSQMsTonLdConPermimp.pdf")
}
Exclude tonal loudness
Set variables
iVars <- c(allVar, eventVar, ambVar, allSharpVar, allTonalVar, allFluctVar, allRoughVar, allImpulsVar)
dVar <- "dHighAnnoyPc"
seeds <- c(49865, 7852, 845961, 410583, 36748)
Hyperparameter tuning
p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)p
Selected hyperparameters
ntree <- 2501
mtry <- as.integer(length(iVars)/1.6)
Run model
Train preliminary model
nperm <- 5
resultsOutSQMs2 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutSQMs2$OOB_RMSE[1] 6.438043resultsOutSQMs2$OOB_MAE[1] 4.933283resultsOutSQMs2$Rsquared[1] 0.6391176Train multiple seeds model
resultsOutSQMs2 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutSQMs2$OOB_RMSE[1] 6.422652resultsOutSQMs2$OOB_MAE[1] 4.923783resultsOutSQMs2$Rsquared[1] 0.6408381
# store results
resdHiAnnoyFitAB['All SQMs no tonal loud', 'RMSE'] <- resultsOutSQMs2$OOB_RMSE
resdHiAnnoyFitAB['All SQMs no tonal loud', 'MAE'] <- resultsOutSQMs2$OOB_MAE
resdHiAnnoyFitAB['All SQMs no tonal loud', 'Rsquared'] <- resultsOutSQMs2$Rsquared
resdHiAnnoyPermImpAB$AllSQMs2 <- resultsOutSQMs2$conditional_permimp
Plot results
par(mai=c(0,3,0,0))
# plot conditional importance
resultsOutSQMs2.conimp <- arrange(resultsOutSQMs2$conditional_permimp, desc(row_number()))
pBar <- ggplot(resultsOutSQMs2.conimp) + geom_col(aes(x=factor(rownames(resultsOutSQMs2.conimp), levels=rownames(resultsOutSQMs2.conimp)), y=CondPermImp), fill=mycolours[7], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 40))
pBar
if (saveplots){
ggsave(filename="PtsABdHiAnnoyAllSQMsNoTonLdConPermimp.svg", width=8, height=2.4, path=file.path(outFigPath, "svg"))
unlink("PtsABdHiAnnoyAllSQMsNoTonLdConPermimp.svg")
ggsave(filename="PtsABdHiAnnoyAllSQMsNoTonLdConPermimp.pdf", width=8, height=2.4, path=file.path(outFigPath, "pdf"))
unlink("PtsABdHiAnnoyAllSQMsNoTonLdConPermimp.pdf")
}
dPsychoacoustic annoyance metrics
Set variables
iVars <- c(ambVar, "dPsychAnnoyWidmann", "dPsychAnnoyMore", "dPsychAnnoyDi", "dPsychAnnoyTorija", "dPsychAnnoyWillemsen", "dPsychAnnoyBoucher", "UASPsychAnnoyWidmann", "UASPsychAnnoyMore", "UASPsychAnnoyDi", "UASPsychAnnoyTorija", "UASPsychAnnoyWillemsen", "UASPsychAnnoyBoucher")
dVar <- "dHighAnnoyPc"
seeds <- c(835702, 54, 470912, 652, 55297)
Hyperparameter tuning
p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)p
Selected hyperparameters
ntree <- 4001
mtry <- as.integer(length(iVars)/1.25)
Run model
Train preliminary model
nperm <- 5
resultsOutPA <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutPA$OOB_RMSE[1] 6.809738resultsOutPA$OOB_MAE[1] 5.143005resultsOutPA$Rsquared[1] 0.5960921Train multiple seeds model
resultsOutPA <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)
# print model prediction results
resultsOutPA$OOB_RMSE[1] 6.807571resultsOutPA$OOB_MAE[1] 5.140512resultsOutPA$Rsquared[1] 0.5963174
# store results
resdHiAnnoyFitAB['All Psychoacoustic annoyance', 'RMSE'] <- resultsOutPA$OOB_RMSE
resdHiAnnoyFitAB['All Psychoacoustic annoyance', 'MAE'] <- resultsOutPA$OOB_MAE
resdHiAnnoyFitAB['All Psychoacoustic annoyance', 'Rsquared'] <- resultsOutPA$Rsquared
resdHiAnnoyPermImpAB$AllPA <- resultsOutPA$conditional_permimp
Plot results
par(mai=c(0,3,0,0))
# plot conditional importance
resultsOutPA.conimp <- arrange(resultsOutPA$conditional_permimp, desc(row_number()))
pBar <- ggplot(resultsOutPA.conimp) + geom_col(aes(x=factor(rownames(resultsOutPA.conimp), levels=rownames(resultsOutPA.conimp)), y=CondPermImp), fill=mycolours[10], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 70))
pBar
if (saveplots){
ggsave(filename="PtsABdHiAnnoyAllPAConPermimp.svg", width=8, height=4.4, path=file.path(outFigPath, "svg"))
unlink("PtsABdHiAnnoyAllPAConPermimp.svg")
ggsave(filename="PtsABdHiAnnoyAllPAConPermimp.pdf", width=8, height=4.4, path=file.path(outFigPath, "pdf"))
unlink("PtsABdHiAnnoyAllPAConPermimp.pdf")
}
Save the results outputs to file
if (savedata){
utils::write.csv(resdHiAnnoyFitAB, paste(outDataPath, "\\PtsABCRFdHiAnnoyOOBFit.csv", sep=""))
ii <- 0
temp = list()
for (res in resdHiAnnoyPermImpAB){
ii <- ii + 1
temp[[ii]] <- as.data.frame(resdHiAnnoyPermImpAB[ii])
names(temp[[ii]]) <- names(resdHiAnnoyPermImpAB[ii])
}
openxlsx::write.xlsx(temp, paste(outDataPath, "\\PtsABCRFdHiAnnoyConPermimp.xlsx",
sep=""),
rowNames=TRUE)
}
Parts A&B summary
Summary of results for Parts A & B combined
With tonal loudness
Absolute variables
# combine the annoyance perm importance results
# convert each result to a tibble with rownames added to a column, renaming the data column to 'dAnnoy' etc.
resdAnnoyMnAbsPermImpTblAB <- as.data.frame(resdAnnoyMnPermImpAB$AbsSQMs1/max(resdAnnoyMnPermImpAB$AbsSQMs1)) |>
tibble::rownames_to_column(var='Variable')
colnames(resdAnnoyMnAbsPermImpTblAB)[2] <- "dAnnoy"
resdHiAnnoyAbsPermImpTblAB <- as.data.frame(resdHiAnnoyPermImpAB$AbsSQMs1/max(resdHiAnnoyPermImpAB$AbsSQMs1)) |>
tibble::rownames_to_column(var='Variable')
colnames(resdHiAnnoyAbsPermImpTblAB)[2] <- "dHiAnnoy"
# merge the dataframes
resAbsPermImpTblAB <- list(resdAnnoyMnAbsPermImpTblAB, resdHiAnnoyAbsPermImpTblAB) |>
purrr::reduce(merge, by = c('Variable'), all = T)
# rename the columns
colnames(resAbsPermImpTblAB)[2:3] <- c("Mean change in annoyance", "%HA | HA' (amb.)")
resAbsPermImpTblAB[is.na(resAbsPermImpTblAB)] <- 0
resAbsAB <- tidyr::pivot_longer(resAbsPermImpTblAB, cols=-Variable, names_to="Outcome", values_to="Imp")
# reorder res tibble, descending by the variable Imp grouped sum and create column with new group order as a factor
resAbsAB <- resAbsAB |> mutate(Variable_sum = sum(Imp), .by=Variable) |> arrange(desc(Variable_sum)) |> group_by(Variable_sum, Variable) |>
mutate(Order = cur_group_id()) |> mutate(Order = as.factor(Order)) |> arrange(desc(Order))
# Reorder outcome levels
resAbsAB$Outcome <- factor(resAbsAB$Outcome, levels=c("Mean change in annoyance", "%HA | HA' (amb.)"))
# plot res as horizontal bar chart, with Imp as y axis, Variable as x axis, Outcome as fill, and Variable_sum as order, relabel x axis with Variable names
pBar <- ggplot(resAbsAB) + geom_col(aes(fill=Outcome, y=Imp, x=Order), colour='grey35', linewidth=0, width=0.75, show.legend=TRUE) + labs(x="Variable", y="Normalised conditional variable\npermutation importance") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 1)) + scale_fill_manual(values=mycolours, labels=c(expression(paste(bar(Delta~A))), "%HA | HA' (amb.)")) + scale_x_discrete(labels=unique(rev(resAbsAB$Variable))) + guides(fill=guide_legend(title='Outcome'))
pBar + scale_y_continuous(breaks=seq(0, 1, by=0.5))
if (saveplots){
ggsave(filename="PtsABcrfAbsSQMsSummary.svg", width=8, height=4, path=file.path(outFigPath, "svg"))
unlink("PtsABcrfAbsSQMsSummary.svg")
ggsave(filename="PtsABcrfAbsSQMsSummary.pdf", width=8, height=4, path=file.path(outFigPath, "pdf"))
unlink("PtsABcrfAbsSQMsSummary.pdf")
}
# plot res as horizontal bar chart, with Imp as y axis, Variable as x axis, Outcome as fill, and Variable_sum as order, relabel x axis with Variable names
pBar <- ggplot(resAbsAB) + geom_col(aes(fill=Outcome, y=Imp, x=Order), colour='grey35', linewidth=0, width=0.75, show.legend=TRUE) + labs(x="Variable", y="Normalised conditional variable\npermutation importance") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2), legend.position = "top") + coord_flip(ylim=c(0, 1)) + scale_fill_manual(values=mycolours, labels=c(expression(paste(bar(Delta~A))), "%HA | HA' (amb.)")) + scale_x_discrete(labels=unique(rev(resAbsAB$Variable))) + guides(fill=guide_legend(title='Outcome', nrow=2, ncol=1))
pBar + scale_y_continuous(breaks=seq(0, 1, by=0.5))
if (saveplots){
ggsave(filename="PtsABcrfAbsSQMsSummaryNw.svg", width=4, height=4, path=file.path(outFigPath, "svg"))
unlink("PtsABcrfAbsSQMsSummary.svg")
ggsave(filename="PtsABcrfAbsSQMsSummaryNw.pdf", width=4, height=4, path=file.path(outFigPath, "pdf"))
unlink("PtsABcrfAbsSQMsSummary.pdf")
}
All variables
# combine the annoyance perm importance results
# convert each result to a tibble with rownames added to a column, renaming the data column to 'dAnnoy' etc.
resdAnnoyMnAllPermImpTblAB <- as.data.frame(resdAnnoyMnPermImpAB$AllSQMs1/max(resdAnnoyMnPermImpAB$AllSQMs1)) |>
tibble::rownames_to_column(var='Variable')
colnames(resdAnnoyMnAllPermImpTblAB)[2] <- "dAnnoy"
resdHiAnnoyAllPermImpTblAB <- as.data.frame(resdHiAnnoyPermImpAB$AllSQMs1/max(resdHiAnnoyPermImpAB$AllSQMs1)) |>
tibble::rownames_to_column(var='Variable')
colnames(resdHiAnnoyAllPermImpTblAB)[2] <- "dHiAnnoy"
# merge the dataframes
resAllPermImpTblAB <- list(resdAnnoyMnAllPermImpTblAB, resdHiAnnoyAllPermImpTblAB) |>
purrr::reduce(merge, by = c('Variable'), all = T)
# rename the columns
colnames(resAllPermImpTblAB)[2:3] <- c("Mean change in annoyance", "%HA | HA' (amb.)")
resAllPermImpTblAB[is.na(resAllPermImpTblAB)] <- 0
resAllAB <- tidyr::pivot_longer(resAllPermImpTblAB, cols=-Variable, names_to="Outcome", values_to="Imp")
# reorder res tibble, descending by the variable Imp grouped sum and create column with new group order as a factor
resAllAB <- resAllAB |> mutate(Variable_sum = sum(Imp), .by=Variable) |> arrange(desc(Variable_sum)) |> group_by(Variable_sum, Variable) |>
mutate(Order = cur_group_id()) |> mutate(Order = as.factor(Order)) |> arrange(desc(Order))
# Reorder outcome levels
resAllAB$Outcome <- factor(resAllAB$Outcome, levels=c("Mean change in annoyance", "%HA | HA' (amb.)"))
# plot res as horizontal bar chart, with Imp as y axis, Variable as x axis, Outcome as fill, and Variable_sum as order, relabel x axis with Variable names
pBar <- ggplot(resAllAB) + geom_col(aes(fill=Outcome, y=Imp, x=Order), colour='grey35', linewidth=0, width=0.75, show.legend=TRUE) + labs(x="Variable", y="Normalised conditional variable permutation importance") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2), legend.position = "right") + coord_flip(ylim=c(-0.1, 1.1)) + scale_fill_manual(values=mycolours) + scale_x_discrete(labels=unique(rev(resAllAB$Variable)))
pBar + scale_y_continuous(breaks=seq(0, 1, by=0.5))
if (saveplots){
ggsave(filename="PtsABcrfAllSQMsSummary.svg", width=8, height=3, path=file.path(outFigPath, "svg"))
unlink("PtsABcrfAllSQMsSummary.svg")
ggsave(filename="PtsABcrfAllSQMsSummary.pdf", width=8, height=3, path=file.path(outFigPath, "pdf"))
unlink("PtsABcrfAllSQMsSummary.pdf")
}
No tonal loudness
Absolute variables
# combine the annoyance perm importance results
# convert each result to a tibble with rownames added to a column, renaming the data column to 'dAnnoy' etc.
resdAnnoyMnAbsPermImpNoTonLdTblAB <- as.data.frame(resdAnnoyMnPermImpAB$AbsSQMs2/max(resdAnnoyMnPermImpAB$AbsSQMs2)) |>
tibble::rownames_to_column(var='Variable')
colnames(resdAnnoyMnAbsPermImpNoTonLdTblAB)[2] <- "dAnnoy"
resdHiAnnoyAbsPermImpNoTonLdTblAB <- as.data.frame(resdHiAnnoyPermImpAB$AbsSQMs2/max(resdHiAnnoyPermImpAB$AbsSQMs2)) |>
tibble::rownames_to_column(var='Variable')
colnames(resdHiAnnoyAbsPermImpNoTonLdTblAB)[2] <- "dHiAnnoy"
# merge the dataframes
resAbsPermImpNoTonLdTblAB <- list(resdAnnoyMnAbsPermImpNoTonLdTblAB, resdHiAnnoyAbsPermImpNoTonLdTblAB) |>
purrr::reduce(merge, by = c('Variable'), all = T)
# rename the columns
colnames(resAbsPermImpNoTonLdTblAB)[2:3] <- c("Mean change in annoyance", "%HA | HA' (amb.)")
resAbsPermImpNoTonLdTblAB[is.na(resAbsPermImpNoTonLdTblAB)] <- 0
resAbsNoTonLdAB <- tidyr::pivot_longer(resAbsPermImpNoTonLdTblAB, cols=-Variable, names_to="Outcome", values_to="Imp")
# reorder res tibble, descending by the variable Imp grouped sum and create column with new group order as a factor
resAbsNoTonLdAB <- resAbsNoTonLdAB |> mutate(Variable_sum = sum(Imp), .by=Variable) |> arrange(desc(Variable_sum)) |> group_by(Variable_sum, Variable) |>
mutate(Order = cur_group_id()) |> mutate(Order = as.factor(Order)) |> arrange(desc(Order))
# Reorder outcome levels
resAbsNoTonLdAB$Outcome <- factor(resAbsNoTonLdAB$Outcome, levels=c("Mean change in annoyance", "%HA | HA' (amb.)"))
# plot res as horizontal bar chart, with Imp as y axis, Variable as x axis, Outcome as fill, and Variable_sum as order, relabel x axis with Variable names
pBar <- ggplot(resAbsNoTonLdAB) + geom_col(aes(fill=Outcome, y=Imp, x=Order), colour='grey35', linewidth=0, width=0.75, show.legend=TRUE) + labs(x="Variable", y="Normalised conditional variable permutation importance") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2), legend.position = "right") + coord_flip(ylim=c(-0.1, 1.1)) + scale_fill_manual(values=mycolours) + scale_x_discrete(labels=unique(rev(resAbsNoTonLdAB$Variable)))
pBar + scale_y_continuous(breaks=seq(0, 1, by=0.5))
if (saveplots){
ggsave(filename="PtsABcrfAbsSQMsNoTonLdSummary.svg", width=8, height=3, path=file.path(outFigPath, "svg"))
unlink("PtsABcrfAbsSQMsNoTonLdSummary.svg")
ggsave(filename="PtsABcrfAbsSQMsNoTonLdSummary.pdf", width=8, height=3, path=file.path(outFigPath, "pdf"))
unlink("PtsABcrfAbsSQMsNoTonLdSummary.pdf")
}
All variables
# combine the annoyance perm importance results
# convert each result to a tibble with rownames added to a column, renaming the data column to 'dAnnoy' etc.
resdAnnoyMnAllPermImpNoTonLdTblAB <- as.data.frame(resdAnnoyMnPermImpAB$AllSQMs2/max(resdAnnoyMnPermImpAB$AllSQMs2)) |>
tibble::rownames_to_column(var='Variable')
colnames(resdAnnoyMnAllPermImpNoTonLdTblAB)[2] <- "dAnnoy"
resdHiAnnoyAllPermImpNoTonLdTblAB <- as.data.frame(resdHiAnnoyPermImpAB$AllSQMs2/max(resdHiAnnoyPermImpAB$AllSQMs2)) |>
tibble::rownames_to_column(var='Variable')
colnames(resdHiAnnoyAllPermImpNoTonLdTblAB)[2] <- "dHiAnnoy"
# merge the dataframes
resAllPermImpNoTonLdTblAB <- list(resdAnnoyMnAllPermImpNoTonLdTblAB, resdHiAnnoyAllPermImpNoTonLdTblAB) |>
purrr::reduce(merge, by = c('Variable'), all = T)
# rename the columns
colnames(resAllPermImpNoTonLdTblAB)[2:3] <- c("Mean change in annoyance", "%HA | HA' (amb.)")
resAllPermImpNoTonLdTblAB[is.na(resAllPermImpNoTonLdTblAB)] <- 0
resAllNoTonLdAB <- tidyr::pivot_longer(resAllPermImpNoTonLdTblAB, cols=-Variable, names_to="Outcome", values_to="Imp")
# reorder res tibble, descending by the variable Imp grouped sum and create column with new group order as a factor
resAllNoTonLdAB <- resAllNoTonLdAB |> mutate(Variable_sum = sum(Imp), .by=Variable) |> arrange(desc(Variable_sum)) |> group_by(Variable_sum, Variable) |>
mutate(Order = cur_group_id()) |> mutate(Order = as.factor(Order)) |> arrange(desc(Order))
# Reorder outcome levels
resAllNoTonLdAB$Outcome <- factor(resAllNoTonLdAB$Outcome, levels=c("Mean change in annoyance", "%HA | HA' (amb.)"))
# plot res as horizontal bar chart, with Imp as y axis, Variable as x axis, Outcome as fill, and Variable_sum as order, relabel x axis with Variable names
pBar <- ggplot(resAllNoTonLdAB) + geom_col(aes(fill=Outcome, y=Imp, x=Order), colour='grey35', linewidth=0, width=0.75, show.legend=TRUE) + labs(x="Variable", y="Normalised conditional variable permutation importance") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2), legend.position = "right") + coord_flip(ylim=c(-0.1, 1.1)) + scale_fill_manual(values=mycolours) + scale_x_discrete(labels=unique(rev(resAllNoTonLdAB$Variable)))
pBar + scale_y_continuous(breaks=seq(0, 1, by=0.5))
if (saveplots){
ggsave(filename="PtsABcrfAllSQMsNoTonLdSummary.svg", width=8, height=3, path=file.path(outFigPath, "svg"))
unlink("PtsABcrfAllSQMsNoTonLdSummary.svg")
ggsave(filename="PtsABcrfAllSQMsNoTonLdSummary.pdf", width=8, height=3, path=file.path(outFigPath, "pdf"))
unlink("PtsABcrfAllSQMsNoTonLdSummary.pdf")
}
Save the results outputs to file
# Make a list of the summary results
resSummary <- list(resAbsAB, resAllAB, resAbsNoTonLdAB, resAllNoTonLdAB)
# Save the results
if (savedata){
ii <- 0
temp = list()
for (res in resSummary){
ii <- ii + 1
temp[[ii]] <- data.frame(resSummary[ii])
}
openxlsx::write.xlsx(temp, paste(outDataPath, "\\PtsABCRFSummary.xlsx",
sep=""),
rowNames=TRUE)
}
---
title: "REFMAP Listening test 1 Parts A & B analysis: Random forest variable importance identification - Revision 1"
output: html_notebook
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

This notebook analyses both parts of the data in terms of variable importance, using a random forest model based on conditional inference trees and a conditional permutation variable importance algorithm.

# Setup

## Load packages

```{r}
# load packages
library(tidyr)
library(ggplot2)
library(party)
library(conflicted)
library(tidyverse)
library(openxlsx)
library(caret)
library(viridis)
library(cowplot)
library(permimp)
# set package parameters
theme_set(theme_bw())

```

```{r}

# plot colour scheme

mycolourlist = list(c(0, 102, 255), c(0, 204, 153), c(255, 0, 102), c(74, 111, 152), c(251, 164, 49), c(204, 153, 255), c(90, 192, 255), c(80, 245, 233), c(255, 90, 192), c(164, 201, 242), c(255, 254, 139), c(255, 243, 255))
mycolours = matrix()

for (ii in 1:length(mycolourlist)){
  mycolours[ii] = rgb(mycolourlist[[ii]][1]/255,
                      mycolourlist[[ii]][2]/255,
                      mycolourlist[[ii]][3]/255)
}

# toggle to save plots
saveplots = TRUE

if (saveplots){
  # set output plot directory
  choose.files(caption="Just cancel this", filters=matrix(data=c(" ", " "), ncol=2))  # workaround for bug in RTerm choose.dir
  outFigPath <- utils::choose.dir(caption="Select output folder to save plots '03 Experiment\\Experiment 1\\Analysis\\Plots'")
  
  if (!dir.exists(file.path(outFigPath, "svg"))){dir.create(file.path(outFigPath, "svg"))}
  if (!dir.exists(file.path(outFigPath, "pdf"))){dir.create(file.path(outFigPath, "pdf"))}
  
}

# toggle to save data
savedata = TRUE

if (savedata){
  # set output plot directory
  if (saveplots==FALSE){
    choose.files(caption="Just cancel this", filters=matrix(data=c(" ", " "), ncol=2))  # workaround for bug in RTerm choose.dir
  }
  outDataPath <- utils::choose.dir(caption="Select output folder to save data '03 Experiment\\Experiment 1\\Analysis\\R'")
}
 

```

# Import data and wrangle

```{r}

stimDatapath <- utils::choose.files(caption=r"(Select refmap_listest1_testdata_ByStim.csv from 03 Experiment\Experiment 1\Analysis\PostProcess)",
                                     filters=matrix(data=c("refmap_listest1_testdata_ByStim.csv", "refmap_listest1_testdata_ByStim.csv"), ncol=2))

stimData <- utils::read.csv(stimDatapath, header=TRUE)

colnames(stimData)[1] <- "Stimulus"

# make response proportions into percentages
stimData[['HighAnnoyPc']] <- stimData[['HighAnnoyProp']]*100
stimData[['dHighAnnoyPc']] <- stimData[['dHighAnnoyProp']]*100

```

```{r}
# function to encode categorical to ordinal numeric variables
encode_ordinal <- function(x, order=unique(x)) {
  x <- as.numeric(factor(x, levels=order, exclude=NULL, order=TRUE))
  x
}

# definition of ordinal variable levels
SNRCats <- c("No UAS", "-16", "-10", "-4", "2", "8")
UASLAeqCats <- c("No UAS", "42", "48", "54", "60")

```

The aggregated data by stimulus are assigned to a dataframe, relevant categorical variables are converted to ordinal, and then the variable subset of interest is selected, NA rows dropped (ie, the 'no UAS' stimuli, as the conditional variable importance algorithm cannot currently handle NA values, which are present in all the UAS dB metrics), and a formula assigned.

```{r}

stimDataNum <- data.frame()

stimDataNum <- cbind(stimData[, 'Stimulus'],
                     stimData[, "UASEvents"],
                     stimData[, which(colnames(stimData)=="UASLAeq"):
                                which(colnames(stimData)=="SNRlevel")],
                     stimData[, which(colnames(stimData)=="IntermitRatioC2MaxLR"):
                                which(colnames(stimData)=="IntermitRatioC5MaxLR")],
                     stimData[, which(colnames(stimData)=="UASLAEMaxLR"):
                                which(colnames(stimData)=="UASEPNLMaxLR")],
                     stimData[, which(colnames(stimData)=="UASLoudECMAPowAvgBin"):
                                which(colnames(stimData)=="UASLoudISO3PowAvgBin")],
                     stimData[, which(colnames(stimData)=="UASTonalECMAAvgMaxLR"):
                                which(colnames(stimData)=="UASSharpvBISO105ExBin")],
                     stimData[, which(colnames(stimData)=="UASImpulsSHMPowAvgMaxLR"):
                                which(colnames(stimData)=="UASPsychAnnoyBoucher")],
                     stimData[, which(colnames(stimData)=="LAeqLAF90diff"):
                                which(colnames(stimData)=="dPsychAnnoyBoucher")],
                     stimData[, which(colnames(stimData)=="ValenceMedian"):
                                which(colnames(stimData)=="dHighAnnoyProp")],
                     stimData[, which(colnames(stimData)=="HighAnnoyPc"):
                                which(colnames(stimData)=="dHighAnnoyPc")])

# remove duplicated variables
stimDataNum <- subset(stimDataNum, select = -c(UASLAeq))

colnames(stimDataNum)[1] <- "Stimulus"
colnames(stimDataNum)[2] <- "UASEvents"

# make the discrete ordinal outcome variables factors
stimDataNum[['UASEvents']] <- factor(stimDataNum[['UASEvents']], levels=c(0, 1, 3, 5, 9), order=TRUE)
stimDataNum[['ValenceMedian']] <- factor(stimDataNum[['ValenceMedian']], levels=c(1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5), order=TRUE)
stimDataNum[['ArousalMedian']] <- factor(stimDataNum[['ArousalMedian']], levels=c(1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5), order=TRUE)
stimDataNum[['AnnoyMedian']] <- factor(stimDataNum[['AnnoyMedian']], levels=c(0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5,
                                                                                5.5, 6, 6.5, 7, 7.5, 8, 8.5, 9, 9.5, 10), order=TRUE)
stimDataNum[['dValenceMedian']] <- factor(stimDataNum[['dValenceMedian']], levels=c(-4, -3.5, -3, -2.5, -2, -1.5, -1, -0.5, 0,
                                                                                      0.5, 1, 1.5, 2, 2.5, 3, 3.5,  4), order=TRUE)
stimDataNum[['dArousalMedian']] <- factor(stimDataNum[['dArousalMedian']], levels=c(-4, -3.5, -3, -2.5, -2, -1.5, -1, -0.5, 0,
                                                                                      0.5, 1, 1.5, 2, 2.5, 3, 3.5,  4), order=TRUE)
stimDataNum[['dAnnoyMedian']] <- factor(stimDataNum[['dAnnoyMedian']], levels=c(-10, -9.5, -9, -8.5, -8, -7.5, -7, -6.5, -6, -5.5, -5,
                                                                                  -4.5, -4, -3.5, -3, -2.5, -2, -1.5, -1, -0.5,
                                                                                 0, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5,
                                                                                 5.5, 6, 6.5, 7, 7.5, 8, 8.5, 9, 9.5, 10), order=TRUE)

# omit ambient-only stimuli
stimDataNum <- stimDataNum |> dplyr::filter(UASEvents != 0)


stimDataNum$SNRlevel <- as.numeric(stimDataNum$SNRlevel)
```

# Random forest functions

Write a function to train a conditional-inference random forest (crf) model on input data according to input formula, iterate over input random seeds, average error and variable importance metrics, and output metrics with plotted

## Averaging over multiple random seeds

```{r}

multi_crfReg <- function(dataIn, iVars, dVar, seeds, ntree, mtry, permImpCondThres=0.95, minsplit=20, minbucket=7, nperm=1){
  # initialise variables
  crfOOBErrAll <- 0
  crfOOBRMSE <- 0
  crfOOBMAE <- 0
  crfOOBErrR2 <- 0
  crfMarPermImpVals <- 0
  crfConPermImpVals <- 0
  crfMarPermImpValsPerTree <- data.frame()
  crfConPermImpValsPerTree <- data.frame()
  
  for (iters in 1:length(seeds)){
    
    # formula for regression
    formVars <- reformulate(iVars, dVar)
    
    # set random seed
    set.seed(seeds[iters])
    # train crf model
    crfModel <- party::cforest(formVars, data=dataIn,
                               controls=party::cforest_unbiased(ntree=ntree,
                                                                mtry=mtry,
                                                                minsplit=minsplit,
                                                                minbucket=minbucket))
    
    # get OOB predictions
    crfModelOOB <- predict(crfModel, OOB=TRUE, type='response')
    
    # get OOB error
    crfModelOOBErr <- as.numeric(as.matrix(as.numeric(as.matrix(crfModelOOB))
                                           - as.numeric(as.matrix(crfModel@data@env$response[[names(crfModel@data@env$response)]]))))

    # OOB RMSE, MAE and Rsquared
    crfOOBRMSE <- crfOOBRMSE + sqrt(mean(crfModelOOBErr^2))
    crfOOBMAE <- crfOOBMAE + mean(abs(crfModelOOBErr))
    crfOOBErrR2 <- crfOOBErrR2 + cor(as.numeric(as.matrix(crfModelOOB)),
                                     as.numeric(as.matrix(crfModel@data@env$response[[names(crfModel@data@env$response)]])))^2

    # set random seed
    set.seed(seeds[iters])

    # set random seed
    set.seed(seeds[iters])
    # conditional variable permutation importance
    crfConPermImp <- permimp::permimp(crfModel, nperm=nperm, conditional=TRUE, threshold=permImpCondThres, progressBar=FALSE)
    
    crfConPermImpVals <- crfConPermImpVals + crfConPermImp$values
    crfConPermImpValsPerTree <- rbind(crfConPermImpValsPerTree, crfConPermImp$perTree)
  }
  
  # average metrics
  crfOOBErrAll <- crfOOBErrAll/length(seeds)
  crfOOBRMSE <- crfOOBRMSE/length(seeds)
  crfOOBMAE <- crfOOBMAE/length(seeds)
  crfOOBErrR2 <- crfOOBErrR2/length(seeds)
  crfConPermImpVals <- data.frame(CondPermImp=sort(crfConPermImpVals/length(seeds), decreasing=TRUE))
  crfConPermImpValsQtl <- data.frame(apply(crfConPermImpValsPerTree, 2, quantile, probs=c(0.25, 0.50, 0.75)))
  
  resultsOut <- list('OOB_RMSE'=crfOOBRMSE, 'OOB_MAE'=crfOOBMAE, 'Rsquared'=crfOOBErrR2, 'conditional_permimp'=crfConPermImpVals,                      'conditional_permimp_perTree'=crfConPermImpValsPerTree, 'conditional_permimp_qtl'=crfConPermImpValsQtl)
  return(resultsOut)
}

```

## Comparing rankings from two seeds 

```{r}

crfReg <- function(dataIn, iVars, dVar, seeds, ntree, mtry, permImpCondThres=0.95, minsplit=20, minbucket=7, nperm=1){
  # initialise variables
  crfOOBErrAll <- 0
  crfOOBRMSE <- 0
  crfOOBMAE <- 0
  crfOOBErrR2 <- 0
  crfMarPermImpVals <- 0
  crfConPermImpVals <- 0
  crfMarPermImpValsPerTree <- data.frame()
  crfConPermImpValsPerTree <- data.frame()

  # formula for regression
  formVars <- reformulate(iVars, dVar)
  
  for (iters in 1:length(seeds)){
  
    # set random seed
    set.seed(seeds[iters])
    # train crf model
    crfModel <- party::cforest(formVars, data=dataIn,
                               controls=party::cforest_unbiased(ntree=ntree,
                                                                mtry=mtry,
                                                                minsplit=minsplit,
                                                                minbucket=minbucket))
    
    # conditional variable permutation importance
    crfConPermImp <- permimp::permimp(crfModel, nperm=nperm, conditional=TRUE, threshold=permImpCondThres, progressBar=FALSE)
    
    crfConPermImpVals <- crfConPermImp$values
    
    if (iters == 1){
      crfConPermImpVals1 <- data.frame(CondPermImp=sort(crfConPermImpVals, decreasing=TRUE))
      crfConPermImpValsPerTree1 <- crfConPermImp$perTree
      crfConPermImpValsQtl1 <- data.frame(apply(crfConPermImpValsPerTree1, 2, quantile, probs=c(0.25, 0.50, 0.75)))
      
      # get OOB predictions
      crfModelOOB <- predict(crfModel, OOB=TRUE, type='response')
      
      # get OOB error
      crfModelOOBErr <- as.numeric(as.matrix(as.numeric(as.matrix(crfModelOOB))
                                              - as.numeric(as.matrix(crfModel@data@env$response[[names(crfModel@data@env$response)]]))))
      
      # OOB RMSE, error quartiles and Rsquared
      crfOOBRMSE <- sqrt(mean(crfModelOOBErr^2))
      crfOOBMAE <- crfOOBMAE + mean(abs(crfModelOOBErr))
      crfOOBErrR2 <- cor(as.numeric(as.matrix(crfModelOOB)),
                                    as.numeric(as.matrix(crfModel@data@env$response[[names(crfModel@data@env$response)]])))^2

      }
    
    else{
      crfConPermImpValsN <- data.frame(CondPermImp=sort(crfConPermImpVals, decreasing=TRUE))
      
      nVarImpChecks <- min(max(sum(crfConPermImpVals1 >= crfConPermImpVals1$CondPermImp[1]*0.1),
                               sum(crfConPermImpValsN >= crfConPermImpValsN$CondPermImp[1]*0.1)), 4)  # number of variable importance values with a value at least 10% of the highest importance
      if (sum(rownames(crfConPermImpVals1)[1:nVarImpChecks] != rownames(crfConPermImpValsN)[1:nVarImpChecks]) > 0){
        warning("Permutation importance rank order within 10% of max differs between seeds: increase number of trees ('ntree') or number of permutations ('nperm'), or subsample of features ('mtry')")
      }
      else{
        resultsOut <- list('OOB_errors'=crfModelOOBErr, 'OOB_RMSE'=crfOOBRMSE, 'OOB_MAE'=crfOOBMAE, 'Rsquared'=crfOOBErrR2, 'conditional_permimp'=crfConPermImpVals1, 'conditional_permimp_perTree'=crfConPermImpValsPerTree1, 'conditional_permpimp_qtl'=crfConPermImpValsQtl1)
        return(resultsOut)
      }
      
    }
    
  }

}

```

## Hyperparameter tuning

```{r, fig.width=12, fig.height=4}
mtryTune <- function(dataIn, iVars, dVar, seeds, ntrees, minsplit=20, minbucket=7){

  formVars <- reformulate(iVars, dVar)
  
  # set mtry values and corresponding iVars/mtry ratios
  if (length(iVars) > 9){
    iVars_mtrys <- c(10.5, 5.25, 3.5, 2.75, 2.25, 1.75, 1.5, 1.25)
    mtrys <- as.integer(length(iVars)/iVars_mtrys)
  }
  else{
    mtrys <- seq(2, length(iVars) - 3, by=1)
    iVars_mtrys <- length(iVars)/mtrys
  }
  iVars_mtrys <- iVars_mtrys[mtrys >= 2]  # remove 0 or 1 values
  mtrys <- mtrys[mtrys >= 2]  # remove 0 or 1 values
  
  # remove any duplicated values
  iVars_mtrys <- iVars_mtrys[!(duplicated(mtrys) | duplicated(mtrys, fromLast = TRUE))]
  mtrys <- mtrys[!(duplicated(mtrys) | duplicated(mtrys, fromLast = TRUE))]

  # ensure mtrys is less than length(iVars)
  iVars_mtrys <- iVars_mtrys[mtrys < length(iVars)]
  mtrys <- mtrys[mtrys < length(iVars)]

  resRMSEMap = matrix(data=0, nrow=length(mtrys), ncol=length(ntrees))
  resRsquaredMap = matrix(data=0, nrow=length(mtrys), ncol=length(ntrees))
  resMAEMap = matrix(data=0, nrow=length(mtrys), ncol=length(ntrees))
  
  
  for (ii in seq(1, length(ntrees))){
    
    tuneMod.results <- data.frame(RMSE=numeric(length(mtrys)),
                                Rsquared=numeric(length(mtrys)),
                                MAE=numeric(length(mtrys)))
    
    for (seed in seeds){
      set.seed(seed)
      ntree = ntrees[ii]
      tuneMod <- caret::train(formVars,
                              data=dataIn,
                              method='cforest',
                              controls=party::cforest_unbiased(ntree=ntree,
                                                               minsplit=minsplit,
                                                               minbucket=minbucket),
                              tuneGrid=data.frame(.mtry=mtrys),
                              trControl = trainControl(method = "oob"))
      
      
      
      # accumulate results
      resRMSEMap[, ii] <- resRMSEMap[, ii] + tuneMod$results$RMSE
      resRsquaredMap[, ii] <- resRsquaredMap[, ii] + tuneMod$results$Rsquared
      resMAEMap[, ii] <- resMAEMap[, ii] + tuneMod$results$MAE
      
      tuneMod.results <- tuneMod.results + tuneMod$results[, which(names(tuneMod$results) != 'mtry')]
    }

    # average results
    tuneMod.results <- tuneMod.results/length(seeds)
    tuneMod.results <- cbind(tuneMod.results, data.frame(mtry=mtrys), data.frame(iVars_mtry=iVars_mtrys))

    print(tuneMod.results)

  }
  
  # average results
  resRMSEMap <- resRMSEMap/length(seeds)
  resRsquaredMap <- resRsquaredMap/length(seeds)
  resMAEMap <- resMAEMap/length(seeds)
  
  
  # convert to data frames with mtry as row names and ntree as column names, and convert to long format using tidyverse
  resdfRMSEMap <- as.data.frame(resRMSEMap)
  rownames(resdfRMSEMap) <- mtrys
  colnames(resdfRMSEMap) <- ntrees
  resdfRsquaredMap <- as.data.frame(resRsquaredMap)
  rownames(resdfRsquaredMap) <- mtrys
  colnames(resdfRsquaredMap) <- ntrees
  resdfMAEMap <- as.data.frame(resMAEMap)
  rownames(resdfMAEMap) <- mtrys
  colnames(resdfMAEMap) <- ntrees
  
  
  # convert dataframes to long format using tidyverse
  resdfRMSEMap <- resdfRMSEMap |>
                      rownames_to_column('mtry') |>
                          gather(key='ntree', value='RMSE', -mtry)
  
  resdfRsquaredMap <- resdfRsquaredMap |>
                          rownames_to_column('mtry') |>
                              gather(key='ntree', value='Rsquared', -mtry)
  
  resdfMAEMap <- resdfMAEMap |>
                    rownames_to_column('mtry') |>
                        gather(key='ntree', value='MAE', -mtry)
  
  # ensure ntree and mtry columns are ordered factors
  resdfRMSEMap$ntree <- factor(resdfRMSEMap$ntree, levels=as.character(ntrees))
  resdfRMSEMap$mtry <- factor(resdfRMSEMap$mtry, levels=as.character(mtrys))
  
  resdfRsquaredMap$ntree <- factor(resdfRsquaredMap$ntree, levels=as.character(ntrees))
  resdfRsquaredMap$mtry <- factor(resdfRsquaredMap$mtry, levels=as.character(mtrys))
  
  resdfMAEMap$ntree <- factor(resdfMAEMap$ntree, levels=as.character(ntrees))
  resdfMAEMap$mtry <- factor(resdfMAEMap$mtry, levels=as.character(mtrys))
  
  # plot heatmaps using ggplot, with extreme (min or max) value plotted as overlaid point using annotate and colourbar scale reversed
  pHeatmapRMSE <- ggplot(resdfRMSEMap) +
                    geom_tile(aes(x=ntree, y=mtry, fill=RMSE)) +
                        scale_fill_viridis(option="viridis", direction=-1) +
                          geom_point(data=resdfRMSEMap[which(resdfRMSEMap$RMSE
                                                             == min(resdfRMSEMap$RMSE),
                                                             arr.ind = TRUE),],
                                     aes(x=ntree, y=mtry), colour="red", size=2) +
                            guides(colour = guide_colourbar(reverse=TRUE)) +
                              labs(x="ntree", y="mtry", fill="RMSE") +
                                theme(text = element_text(family = "serif"))
  
  pHeatmapRsquared <- ggplot(resdfRsquaredMap) +
                        geom_tile(aes(x=ntree, y=mtry, fill=Rsquared)) +
                            scale_fill_viridis(option="viridis", direction=1) +
                              geom_point(data=resdfRsquaredMap[which(resdfRsquaredMap$Rsquared
                                                                     == max(resdfRsquaredMap$Rsquared),
                                                                     arr.ind = TRUE),],
                                         aes(x=ntree, y=mtry), colour="red", size=2) +
                                guides(colour = guide_colourbar(reverse=TRUE)) +
                                  labs(x="ntree", y="mtry", fill="Rsquared") +
                                    theme(text = element_text(family = "serif"))
  
  pHeatmapMAE <- ggplot(resdfMAEMap) +
                    geom_tile(aes(x=ntree, y=mtry, fill=MAE)) +
                        scale_fill_viridis(option="viridis", direction=-1) +
                          geom_point(data=resdfMAEMap[which(resdfMAEMap$MAE
                                                            == min(resdfMAEMap$MAE),
                                                            arr.ind = TRUE),],
                                     aes(x=ntree, y=mtry), colour="red", size=2) +
                            guides(colour = guide_colourbar(reverse=TRUE)) +
                              labs(x="ntree", y="mtry", fill="MAE") +
                                theme(text = element_text(family = "serif"))
  
  p <-  cowplot::plot_grid(pHeatmapRMSE,
                           pHeatmapRsquared,
                           pHeatmapMAE,
                           ncol=3, nrow=1)
  
  return(p)

}  # end of function

```


# Parts A & B analysis


## Set global parameters

```{r}

permImpCondThres <- 0.95
minsplit <- 20
minbucket <- 7
ntrees <- c(251, 501, 1001, 1501, 2501, 4001, 5501)

eventVar <- "UASEvents"
ambVar <- "AmbientLAeq"

```

## Mean change in annoyance

### Initialise results output variables

```{r}
resdAnnoyMnFitAB <- data.frame(RMSE = numeric(),
                               MAE = numeric(),
                               Rsquared = numeric())
resdAnnoyMnPermImpAB <- list()

```


### Absolute variables

#### Set variables

```{r}

iVars <- names(stimDataNum)[which(names(stimDataNum) == 'UASEvents'):which(names(stimDataNum) == 'UASPsychAnnoyBoucher')]
iVars <- iVars[! iVars %in% c('SNRlevel', 'IntermitRatioC2MaxLR', 'IntermitRatioC3MaxLR', 'IntermitRatioC5MaxLR')]

dVar <- "dAnnoyMean"

seeds <- c(578312, 544, 84894, 54654, 153157)

```

#### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
              ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

if (saveplots){
  ggsave(filename="PtsABdAnnoyMnAbsVarsHyperTune.svg", width=12, height=4, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMnAbsVarsHyperTune.svg")

  ggsave(filename="PtsABdAnnoyMnAbsVarsHyperTune.pdf", width=12, height=4, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMnAbsVarsHyperTune.pdf")
}

```

Selected hyperparameters

```{r}

ntree <- 2501
mtry <- as.integer(length(iVars)/1.75)

```

#### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutAbs <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutAbs$OOB_RMSE
resultsOutAbs$OOB_MAE
resultsOutAbs$Rsquared

```

Train multiple seeds model

```{r}

resultsOutAbs <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutAbs$OOB_RMSE
resultsOutAbs$OOB_MAE
resultsOutAbs$Rsquared

```


```{r}

# store results
resdAnnoyMnFitAB['Abs vars', 'RMSE'] <- resultsOutAbs$OOB_RMSE
resdAnnoyMnFitAB['Abs vars', 'MAE'] <- resultsOutAbs$OOB_MAE
resdAnnoyMnFitAB['Abs vars', 'Rsquared'] <- resultsOutAbs$Rsquared
resdAnnoyMnPermImpAB$AbsVars <- resultsOutAbs$conditional_permimp

```

#### Plot results

```{r, fig.width=8,fig.height=13}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutAbs.conimp <- arrange(resultsOutAbs$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutAbs.conimp) + geom_col(aes(x=factor(rownames(resultsOutAbs.conimp), levels=rownames(resultsOutAbs.conimp)), y=CondPermImp), fill=mycolours[1], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) +
  coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdAnnoyMnAbsVarsConPermimp.svg", width=8, height=13, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMnAbsVarsConPermimp.svg")
  
  ggsave(filename="PtsABdAnnoyMnAbsVarsConPermimp.pdf", width=8, height=13, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMnAbsVarsConPermimp.pdf")
}
```


```{r, fig.width=8,fig.height=10}

# Plot only positive values

resultsOutAbs.conimpPtv <- resultsOutAbs.conimp |>
                                          rownames_to_column('Metric') |>
                                                filter_if(is.numeric, all_vars(. > 0)) |>
                                                      column_to_rownames('Metric')

pBar <- ggplot(resultsOutAbs.conimpPtv) + geom_col(aes(x=factor(rownames(resultsOutAbs.conimpPtv), levels=rownames(resultsOutAbs.conimpPtv)), y=CondPermImp), fill=mycolours[1], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdAnnoyMnAbsVarsConPermimpPtv.svg", width=8, height=10, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMnAbsVarsConPermimpPtv.svg")
  
  ggsave(filename="PtsABdAnnoyMnAbsVarsConPermimpPtv.pdf", width=8, height=10, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMnAbsVarsConPermimpPtv.pdf")
}

```

```{r, fig.width=8,fig.height=3}

# Plot only values within 1% of the maximum

resultsOutAbs.conimp1pc <- resultsOutAbs.conimp |>
                                          rownames_to_column('Metric') |>
                                                filter_if(is.numeric, all_vars(. > max(resultsOutAbs.conimp)/100)) |>
                                                      column_to_rownames('Metric')

pBar <- ggplot(resultsOutAbs.conimp1pc) + geom_col(aes(x=factor(rownames(resultsOutAbs.conimp1pc), levels=rownames(resultsOutAbs.conimp1pc)), y=CondPermImp), fill=mycolours[1], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdAnnoyMnAbsVarsConPermimp1pc.svg", width=8, height=3, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMnAbsVarsConPermimp1pc.svg")
  
  ggsave(filename="PtsABdAnnoyMnAbsVarsConPermimp1pc.pdf", width=8, height=3, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMnAbsVarsConPermimp1pc.pdf")
}

```

Selected metric

```{r}

absVar <- "UASLAEMaxLR"

```


### SQM analysis

#### Individual SQMs

##### Sharpness

###### Set variables

```{r}

iVars <- c(absVar, eventVar, ambVar, "UASSharpAurISO3PowAvgBin", "UASSharpAurISO305ExBin", "UASSharpAurSHMPowAvgBin", "UASSharpAurSHM05ExBin", "UASSharpAurISO1PowAvgBin", "UASSharpAurISO105ExBin", "UASSharpvBISO1PowAvgBin", "UASSharpvBISO105ExBin", "UASSharpDINPowAvgBin", "UASSharpDIN05ExBin", "UASSharpAurISO1MedBin",
         "UASTonShpAurSHMPowAvgBin", "UASTonShpAurSHM05ExBin")
dVar <- "dAnnoyMean"

seeds <- c(7041, 905, 4984651, 6513213, 120651)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 251
mtry <- as.integer(length(iVars)/1.5)

```

###### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutSharp <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSharp$OOB_RMSE
resultsOutSharp$OOB_MAE
resultsOutSharp$Rsquared

```

Train multiple seeds model

```{r}

resultsOutSharp <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSharp$OOB_RMSE
resultsOutSharp$OOB_MAE
resultsOutSharp$Rsquared

```


```{r}
# store results
resdAnnoyMnFitAB['Abs sharp', 'RMSE'] <- resultsOutSharp$OOB_RMSE
resdAnnoyMnFitAB['Abs sharp', 'MAE'] <- resultsOutSharp$OOB_MAE
resdAnnoyMnFitAB['Abs sharp', 'Rsquared'] <- resultsOutSharp$Rsquared
resdAnnoyMnPermImpAB$AbsSharp <- resultsOutSharp$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=4.9}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutSharp.conimp <- arrange(resultsOutSharp$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutSharp.conimp) + geom_col(aes(x=factor(rownames(resultsOutSharp.conimp), levels=rownames(resultsOutSharp.conimp)), y=CondPermImp), fill=mycolours[2], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + ggtitle("Sharpness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdAnnoyMnSharpConPermimp.svg", width=8, height=4.9, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMnSharpConPermimp.svg")
  
  ggsave(filename="PtsABdAnnoyMnSharpConPermimp.pdf", width=8, height=4.9, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMnSharpConPermimp.pdf")
}


```

Selected metric

```{r}

sharpVar <- "UASSharpAurISO305ExBin"

```

##### Tonal loudness and tonality

###### Set variables

```{r}

iVars <- c(absVar, eventVar, ambVar, "UASTonalECMAAvgMaxLR", "UASTonalSHMInt05ExMaxLR", "UASTonalSHMIntAvgMaxLR", "UASTonalECMA05ExMaxLR", "UASTonalAwSHMAvgMaxLR",	"UASTonalAwSHM05ExMaxLR",	"UASTonalAwSHMIntAvgMaxLR", 	"UASTonalAwSHMInt05ExMaxLR", "UASTonLdECMAPowAvgBin", "UASTonLdECMA05ExBin", "UASTonalAurAvgMaxLR", "UASTonalAur05ExMaxLR", "UASTonalAur10ExMaxLR",
         "UASTonShpAurSHMPowAvgBin", "UASTonShpAurSHM05ExBin")
dVar <- "dAnnoyMean"

seeds <- c(540, 104798, 456464, 87331, 94564)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 251
mtry <- as.integer(length(iVars)/1.5)

```

###### Run model

Train preliminary model

```{r}
# Tonality with tonal loudness

nperm <- 5

resultsOutTonal1 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal1$OOB_RMSE
resultsOutTonal1$OOB_MAE
resultsOutTonal1$Rsquared
```

Train multiple seeds model

```{r}
# Tonality with tonal loudness

resultsOutTonal1 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal1$OOB_RMSE
resultsOutTonal1$OOB_MAE
resultsOutTonal1$Rsquared

```

```{r}
# store results
resdAnnoyMnFitAB['Abs tonal inc loud', 'RMSE'] <- resultsOutTonal1$OOB_RMSE
resdAnnoyMnFitAB['Abs tonal inc loud', 'MAE'] <- resultsOutTonal1$OOB_MAE
resdAnnoyMnFitAB['Abs tonal inc loud', 'Rsquared'] <- resultsOutTonal1$Rsquared
resdAnnoyMnPermImpAB$AbsTonal1 <- resultsOutTonal1$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=5}

par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutTonal1.conimp <- arrange(resultsOutTonal1$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutTonal1.conimp) + geom_col(aes(x=factor(rownames(resultsOutTonal1.conimp), levels=rownames(resultsOutTonal1.conimp)), y=CondPermImp), fill=mycolours[3], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + ggtitle("Tonality inc. tonal loudness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 1.4))
pBar

if (saveplots){
  ggsave(filename="PtsABdAnnoyMnTonalLdConPermimp.svg", width=8, height=5, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMnTonalLdConPermimp.svg")
  
  ggsave(filename="PtsABdAnnoyMnTonalLdConPermimp.pdf", width=8, height=5, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMnTonalLdConPermimp.pdf")
}

```

Selected metric

```{r}

tonLdVar <- "UASTonLdECMAPowAvgBin"

```

##### Tonality without tonal loudness or tonal sharpness

###### Set variables

```{r}

iVars <- c(absVar, eventVar, ambVar, "UASTonalECMAAvgMaxLR", "UASTonalSHMInt05ExMaxLR", "UASTonalSHMIntAvgMaxLR", "UASTonalECMA05ExMaxLR", "UASTonalAwSHMAvgMaxLR",	"UASTonalAwSHM05ExMaxLR",	"UASTonalAwSHMIntAvgMaxLR", "UASTonalAwSHMInt05ExMaxLR",	"UASTonalAurAvgMaxLR", "UASTonalAur05ExMaxLR", "UASTonalAur10ExMaxLR")
dVar <- "dAnnoyMean"

seeds <- c(156089, 5860, 10528, 89541, 4685146)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 501
mtry <- as.integer(length(iVars)/1.25)

```

###### Run model

Train preliminary model

```{r}
# Tonality

nperm <- 5

resultsOutTonal2 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal2$OOB_RMSE
resultsOutTonal2$OOB_MAE
resultsOutTonal2$Rsquared

```

Train multiple seeds model

```{r}
# Tonality

resultsOutTonal2 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal2$OOB_RMSE
resultsOutTonal2$OOB_MAE
resultsOutTonal2$Rsquared

```


```{r}

# store results
resdAnnoyMnFitAB['Abs tonal no loud', 'RMSE'] <- resultsOutTonal2$OOB_RMSE
resdAnnoyMnFitAB['Abs tonal no loud', 'MAE'] <- resultsOutTonal2$OOB_MAE
resdAnnoyMnFitAB['Abs tonal no loud', 'Rsquared'] <- resultsOutTonal2$Rsquared
resdAnnoyMnPermImpAB$AbsTonal2 <- resultsOutTonal2$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=4.4}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutTonal2.conimp <- arrange(resultsOutTonal2$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutTonal2.conimp) + geom_col(aes(x=factor(rownames(resultsOutTonal2.conimp), levels=rownames(resultsOutTonal2.conimp)), y=CondPermImp), fill=mycolours[3], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + ggtitle("Tonality w/o tonal loudness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 1.4))
pBar

if (saveplots){
  ggsave(filename="PtsABdAnnoyMnTonalConPermimp.svg", width=8, height=4.4, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMnTonalConPermimp.svg")
  
  ggsave(filename="PtsABdAnnoyMnTonalConPermimp.pdf", width=8, height=4.4, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMnTonalConPermimp.pdf")
}


```

Selected metric

```{r}

tonalVar <- "UASTonalAwSHMInt05ExMaxLR"

```

##### Fluctuation strength

###### Set variables

```{r}

# Fluctuation strength
iVars <- c(absVar, eventVar, ambVar, "UASFluctOldSHM10ExBin", "UASFluctOldSHM05ExBin", "UASFluctECMA10ExBin", "UASFluctECMA05ExBin", "UASFluctFZ10ExMaxLR", "UASFluctFZ05ExMaxLR", "UASFluctOV10ExMaxLR", "UASFluctOV05ExMaxLR")
dVar <- "dAnnoyMean"

seeds <- c(25107, 546098, 195, 5937, 102658)

```


###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 5501
mtry <- as.integer(length(iVars)/1.5)

```

###### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutFluct <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutFluct$OOB_RMSE
resultsOutFluct$OOB_MAE
resultsOutFluct$Rsquared

```

Train multiple seeds model

```{r}

resultsOutFluct <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutFluct$OOB_RMSE
resultsOutFluct$OOB_MAE
resultsOutFluct$Rsquared

```


```{r}

# store results
resdAnnoyMnFitAB['Abs fluct', 'RMSE'] <- resultsOutFluct$OOB_RMSE
resdAnnoyMnFitAB['Abs fluct', 'MAE'] <- resultsOutFluct$OOB_MAE
resdAnnoyMnFitAB['Abs fluct', 'Rsquared'] <- resultsOutFluct$Rsquared
resdAnnoyMnPermImpAB$AbsFluct <- resultsOutFluct$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=3.5}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutFluct.conimp <- arrange(resultsOutFluct$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutFluct.conimp) + geom_col(aes(x=factor(rownames(resultsOutFluct.conimp), levels=rownames(resultsOutFluct.conimp)), y=CondPermImp), fill=mycolours[4], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + ggtitle("Fluctuation strength") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdAnnoyMnFluctConPermimp.svg", width=8, height=3.5, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMnFluctConPermimp.svg")
  
  ggsave(filename="PtsABdAnnoyMnFluctConPermimp.pdf", width=8, height=3.5, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMnFluctConPermimp.pdf")
}

```

Selected metric

```{r}

fluctVar <- "UASFluctECMA10ExBin"

```

##### Roughness

###### Set variables

```{r}

# Roughness
iVars <- c(absVar, eventVar, ambVar, "UASRoughECMA10ExBin", "UASRoughECMA05ExBin", "UASRoughFZ10ExMaxLR", "UASRoughFZ05ExMaxLR", "UASRoughDW10ExMaxLR", "UASRoughDW05ExMaxLR")
dVar <- "dAnnoyMean"

seeds <- c(4701, 52187, 16589, 65217, 16893)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 1001
mtry <- as.integer(length(iVars)/1.8)

```

###### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutRough <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutRough$OOB_RMSE
resultsOutRough$OOB_MAE
resultsOutRough$Rsquared

```

Train multiple seeds model

```{r}

resultsOutRough <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutRough$OOB_RMSE
resultsOutRough$OOB_MAE
resultsOutRough$Rsquared

```

```{r}
# store results
resdAnnoyMnFitAB['Abs rough', 'RMSE'] <- resultsOutRough$OOB_RMSE
resdAnnoyMnFitAB['Abs rough', 'MAE'] <- resultsOutRough$OOB_MAE
resdAnnoyMnFitAB['Abs rough', 'Rsquared'] <- resultsOutRough$Rsquared
resdAnnoyMnPermImpAB$AbsRough <- resultsOutRough$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=2.9}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutRough.conimp <- arrange(resultsOutRough$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutRough.conimp) + geom_col(aes(x=factor(rownames(resultsOutRough.conimp), levels=rownames(resultsOutRough.conimp)), y=CondPermImp), fill=mycolours[5], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + ggtitle("Roughness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdAnnoyMnRoughConPermimp.svg", width=8, height=2.9, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMnRoughConPermimp.svg")
  
  ggsave(filename="PtsABdAnnoyMnRoughConPermimp.pdf", width=8, height=2.9, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMnRoughConPermimp.pdf")
}


```

Selected metric

```{r}

roughVar <- "UASRoughFZ05ExMaxLR"

```

##### Impulsiveness

###### Set variables

```{r}
# Impulsiveness
iVars <- c(absVar, eventVar, ambVar, "UASImpulsSHMAvgMaxLR", "UASImpulsSHM05ExMaxLR", "UASImpulsSHMPowAvgMaxLR", "UASImpulsLoudWZAvgMaxLR", "UASImpulsLoudWZ05ExMaxLR", "UASImpulsLoudWZPowAvgMaxLR", "UASImpulsLoudWECMAAvgBin", "UASImpulsLoudWECMA05ExBin", "UASImpulsLoudWECMAPowAvgBin")
dVar <- "dAnnoyMean"

seeds <- c(8495, 59867, 5416, 9843, 86)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 4001
mtry <- as.integer(length(iVars)/1.25)

```


###### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutImpuls <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutImpuls$OOB_RMSE
resultsOutImpuls$OOB_MAE
resultsOutImpuls$Rsquared

```

Train multiple seeds model

```{r}

resultsOutImpuls <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutImpuls$OOB_RMSE
resultsOutImpuls$OOB_MAE
resultsOutImpuls$Rsquared

```

```{r}

# store results
resdAnnoyMnFitAB['Abs impuls', 'RMSE'] <- resultsOutImpuls$OOB_RMSE
resdAnnoyMnFitAB['Abs impuls', 'MAE'] <- resultsOutImpuls$OOB_MAE
resdAnnoyMnFitAB['Abs impuls', 'Rsquared'] <- resultsOutImpuls$Rsquared
resdAnnoyMnPermImpAB$AbsImpuls <- resultsOutImpuls$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=3.8}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutImpuls.conimp <- arrange(resultsOutImpuls$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutImpuls.conimp) + geom_col(aes(x=factor(rownames(resultsOutImpuls.conimp), levels=rownames(resultsOutImpuls.conimp)), y=CondPermImp), fill=mycolours[6], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + ggtitle("Impulsiveness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdAnnoyMnImpulsConPermimp.svg", width=8, height=3.8, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMnImpulsConPermimp.svg")
  
  ggsave(filename="PtsABdAnnoyMnImpulsConPermimp.pdf", width=8, height=3.8, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMnImpulsConPermimp.pdf")
}

```

Selected metric

```{r}

impulsVar <- "UASImpulsLoudWZAvgMaxLR"

```

#### SQM and loudness comparison

Now the highest importance SQMs are ranked against each other, controlling for UAS loudness and ambient LAeq.

##### Include tonal loudness

###### Set variables

```{r}

iVars <- c(absVar, eventVar, ambVar, sharpVar, tonLdVar, fluctVar, roughVar, impulsVar)
dVar <- "dAnnoyMean"

seeds <- c(70498, 4, 14986, 453, 864)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 2501
mtry <- as.integer(length(iVars)/1.6)

```

###### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutSQMs1 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs1$OOB_RMSE
resultsOutSQMs1$OOB_MAE
resultsOutSQMs1$Rsquared

```

Train multiple seeds model

```{r}

resultsOutSQMs1 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs1$OOB_RMSE
resultsOutSQMs1$OOB_MAE
resultsOutSQMs1$Rsquared

```

```{r}

# store results
resdAnnoyMnFitAB['Abs SQMs inc tonal loud', 'RMSE'] <- resultsOutSQMs1$OOB_RMSE
resdAnnoyMnFitAB['Abs SQMs inc tonal loud', 'MAE'] <- resultsOutSQMs1$OOB_MAE
resdAnnoyMnFitAB['Abs SQMs inc tonal loud', 'Rsquared'] <- resultsOutSQMs1$Rsquared
resdAnnoyMnPermImpAB$AbsSQMs1 <- resultsOutSQMs1$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=2.4}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutSQMs1.conimp <- arrange(resultsOutSQMs1$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutSQMs1.conimp) + geom_col(aes(x=factor(rownames(resultsOutSQMs1.conimp), levels=rownames(resultsOutSQMs1.conimp)), y=CondPermImp), fill=mycolours[7], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 1.3))
pBar

if (saveplots){
  ggsave(filename="PtsABdAnnoyMnAbsSQMsTonLdConPermimp.svg", width=8, height=2.4, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMnAbsSQMsTonLdConPermimp.svg")
  
  ggsave(filename="PtsABdAnnoyMnAbsSQMsTonLdConPermimp.pdf", width=8, height=2.4, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMnAbsSQMsTonLdConPermimp.pdf")
}

```

##### Exclude tonal loudness

###### Set variables

```{r}

iVars <- c(absVar, eventVar, ambVar, sharpVar, tonalVar, fluctVar, roughVar, impulsVar)
dVar <- "dAnnoyMean"

seeds <- c(546, 57203, 270835, 60592, 8094)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 1501
mtry <- as.integer(length(iVars)/1.6)

```

###### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutSQMs2 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs2$OOB_RMSE
resultsOutSQMs2$OOB_MAE
resultsOutSQMs2$Rsquared

```

Train multiple seeds model

```{r}

resultsOutSQMs2 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs2$OOB_RMSE
resultsOutSQMs2$OOB_MAE
resultsOutSQMs2$Rsquared

```

```{r}

# store results
resdAnnoyMnFitAB['Abs SQMs no tonal loud', 'RMSE'] <- resultsOutSQMs2$OOB_RMSE
resdAnnoyMnFitAB['Abs SQMs no tonal loud', 'MAE'] <- resultsOutSQMs2$OOB_MAE
resdAnnoyMnFitAB['Abs SQMs no tonal loud', 'Rsquared'] <- resultsOutSQMs2$Rsquared
resdAnnoyMnPermImpAB$AbsSQMs2 <- resultsOutSQMs2$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=2.4}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutSQMs2.conimp <- arrange(resultsOutSQMs2$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutSQMs2.conimp) + geom_col(aes(x=factor(rownames(resultsOutSQMs2.conimp), levels=rownames(resultsOutSQMs2.conimp)), y=CondPermImp), fill=mycolours[7], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 1.3))
pBar

if (saveplots){
  ggsave(filename="PtsABdAnnoyMnAbsSQMsNoTonLdConPermimp.svg", width=8, height=2.4, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMnAbsSQMsNoTonLdConPermimp.svg")
  
  ggsave(filename="PtsABdAnnoyMnAbsSQMsNoTonLdConPermimp.pdf", width=8, height=2.4, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMnAbsSQMsNoTonLdConPermimp.pdf")
}

```

#### Psychoacoustic annoyance metrics

##### Set variables

```{r}

iVars <- c(ambVar, "UASPsychAnnoyWidmann", "UASPsychAnnoyMore", "UASPsychAnnoyDi", "UASPsychAnnoyTorija", "UASPsychAnnoyWillemsen", "UASPsychAnnoyBoucher")
dVar <- "dAnnoyMean"

seeds <- c(829, 9, 190, 4564, 924707824)


```

##### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```


Selected hyperparameters

```{r}

ntree <- 1501
mtry <- as.integer(length(iVars)/1.75)

```

##### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutPA <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutPA$OOB_RMSE
resultsOutPA$OOB_MAE
resultsOutPA$Rsquared

```

Train multiple seeds model

```{r}

resultsOutPA <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutPA$OOB_RMSE
resultsOutPA$OOB_MAE
resultsOutPA$Rsquared

```

```{r}

# store results
resdAnnoyMnFitAB['Psychoacoustic annoyance', 'RMSE'] <- resultsOutPA$OOB_RMSE
resdAnnoyMnFitAB['Psychoacoustic annoyance', 'MAE'] <- resultsOutPA$OOB_MAE
resdAnnoyMnFitAB['Psychoacoustic annoyance', 'Rsquared'] <- resultsOutPA$Rsquared
resdAnnoyMnPermImpAB$AbsPA <- resultsOutPA$conditional_permimp

```

##### Plot results

```{r, fig.width=8,fig.height=2.4}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutPA.conimp <- arrange(resultsOutPA$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutPA.conimp) + geom_col(aes(x=factor(rownames(resultsOutPA.conimp), levels=rownames(resultsOutPA.conimp)), y=CondPermImp), fill=mycolours[10], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 1.8))
pBar

if (saveplots){
  ggsave(filename="PtsABdAnnoyMnAbsPAConPermimp.svg", width=8, height=2.4, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMnAbsPAConPermimp.svg")
  
  ggsave(filename="PtsABdAnnoyMnAbsPAConPermimp.pdf", width=8, height=2.4, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMnAbsPAConPermimp.pdf")
}

```

### All variables (absolute and difference)

#### Set variables

```{r}

iVars <- names(stimDataNum)[which(names(stimDataNum) == 'UASEvents'):which(names(stimDataNum) == 'dPsychAnnoyBoucher')]

dVar <- "dAnnoyMean"

seeds <- c(14569, 98651, 54654498, 454948, 41321)

```

#### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
              ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

if (saveplots){
  ggsave(filename="PtsABdAnnoyMnAllVarsHyperTune.svg", width=12, height=4, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMnAllVarsHyperTune.svg")

  ggsave(filename="PtsABdAnnoyMnAllVarsHyperTune.pdf", width=12, height=4, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMnAllVarsHyperTune.pdf")
}

```

Selected hyperparameters

```{r}

ntree <- 2501
mtry <- as.integer(length(iVars)/3.5)

```

#### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutAbsDiffs <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutAbsDiffs$OOB_RMSE
resultsOutAbsDiffs$OOB_MAE
resultsOutAbsDiffs$Rsquared

```

Train multiple seeds model

```{r}

resultsOutAbsDiffs <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutAbsDiffs$OOB_RMSE
resultsOutAbsDiffs$OOB_MAE
resultsOutAbsDiffs$Rsquared
```

```{r}
# store results
resdAnnoyMnFitAB['All vars', 'RMSE'] <- resultsOutAbsDiffs$OOB_RMSE
resdAnnoyMnFitAB['All vars', 'MAE'] <- resultsOutAbsDiffs$OOB_MAE
resdAnnoyMnFitAB['All vars', 'Rsquared'] <- resultsOutAbsDiffs$Rsquared
resdAnnoyMnPermImpAB$AllVars <- resultsOutAbsDiffs$conditional_permimp

```

#### Plot results

```{r, fig.width=8,fig.height=30}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutAbsDiffs.conimp <- arrange(resultsOutAbsDiffs$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutAbsDiffs.conimp) + geom_col(aes(x=factor(rownames(resultsOutAbsDiffs.conimp), levels=rownames(resultsOutAbsDiffs.conimp)), y=CondPermImp), fill=mycolours[9], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdAnnoyMnAllVarsConPermimp.svg", width=8, height=30, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMnAllVarsConPermimp.svg")

  ggsave(filename="PtsABdAnnoyMnAllVarsConPermimp.pdf", width=8, height=30, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMnAllVarsConPermimp.pdf")
}

```

```{r, fig.width=8,fig.height=18}

# Plot only positive values

resultsOutAbsDiffs.conimpPtv <- resultsOutAbsDiffs.conimp |>
                                          rownames_to_column('Metric') |>
                                                filter_if(is.numeric, all_vars(. > 0)) |>
                                                      column_to_rownames('Metric')

pBar <- ggplot(resultsOutAbsDiffs.conimpPtv) + geom_col(aes(x=factor(rownames(resultsOutAbsDiffs.conimpPtv), levels=rownames(resultsOutAbsDiffs.conimpPtv)), y=CondPermImp), fill=mycolours[9], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdAnnoyMnAllVarsConPermimpPtv.svg", width=8, height=18, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMnAllVarsConPermimp.svg")
  
  ggsave(filename="PtsABdAnnoyMnAllVarsConPermimpPtv.pdf", width=8, height=18, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMnAllVarsConPermimp.pdf")
}

```

```{r, fig.width=8,fig.height=6}

# Plot only values within 1% of the maximum

resultsOutAbsDiffs.conimp1pc <- resultsOutAbsDiffs.conimp |>
                                          rownames_to_column('Metric') |>
                                                filter_if(is.numeric, all_vars(. > max(resultsOutAbsDiffs.conimp)/100)) |>
                                                      column_to_rownames('Metric')

pBar <- ggplot(resultsOutAbsDiffs.conimp1pc) + geom_col(aes(x=factor(rownames(resultsOutAbsDiffs.conimp1pc), levels=rownames(resultsOutAbsDiffs.conimp1pc)), y=CondPermImp), fill=mycolours[9], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdAnnoyMnAllVarsConPermimp1pc.svg", width=8, height=6, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMnAllVarsConPermimp1pc.svg")
  
  ggsave(filename="PtsABdAnnoyMnAllVarsConPermimp1pc.pdf", width=8, height=6, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMnAllVarsConPermimp1pc.pdf")
}

```

Selected metric

```{r}

allVar <- "Detect0p1dBIntMaxLR"

```

### dSQM analysis

#### Individual SQMs

##### dSharpness

###### Set variables

```{r}

iVars <- c(allVar, eventVar, ambVar, "dSharpAurISO3PowAvgBin", "dSharpAurISO305ExBin", "dSharpAurSHMPowAvgBin", "dSharpAurSHM05ExBin", "dTonShpAurSHMPowAvgBin", "dTonShpAurSHM05ExBin", "PartTonShpAurSHMPowAvgBin",
           "PartTonShpAurSHM05ExBin", "UASSharpAurISO3PowAvgBin", "UASSharpAurISO305ExBin", "UASSharpAurSHMPowAvgBin", "UASSharpAurSHM05ExBin", "UASSharpAurISO1PowAvgBin", "UASSharpAurISO105ExBin", "UASSharpvBISO1PowAvgBin", "UASSharpvBISO105ExBin", "UASSharpDINPowAvgBin", "UASSharpDIN05ExBin", "UASSharpAurISO1MedBin",
         "UASTonShpAurSHMPowAvgBin", "UASTonShpAurSHM05ExBin")
dVar <- "dAnnoyMean"

seeds <- c(84194, 905, 64815, 928054, 625091)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <-251
mtry <- as.integer(length(iVars)/1.25)

```

###### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutSharp <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSharp$OOB_RMSE
resultsOutSharp$OOB_MAE
resultsOutSharp$Rsquared

```

Train multiple seeds model

```{r}

resultsOutSharp <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSharp$OOB_RMSE
resultsOutSharp$OOB_MAE
resultsOutSharp$Rsquared

```


```{r}

# store results
resdAnnoyMnFitAB['All sharp', 'RMSE'] <- resultsOutSharp$OOB_RMSE
resdAnnoyMnFitAB['All sharp', 'MAE'] <- resultsOutSharp$OOB_MAE
resdAnnoyMnFitAB['All sharp', 'Rsquared'] <- resultsOutSharp$Rsquared
resdAnnoyMnPermImpAB$AllSharp <- resultsOutSharp$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=5.4}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutSharp.conimp <- arrange(resultsOutSharp$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutSharp.conimp) + geom_col(aes(x=factor(rownames(resultsOutSharp.conimp), levels=rownames(resultsOutSharp.conimp)), y=CondPermImp), fill=mycolours[2], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + ggtitle("All sharpness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdAnnoyMnAllSharpConPermimp.svg", width=8, height=5.4, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMnAllSharpConPermimp.svg")
  
  ggsave(filename="PtsABdAnnoyMnAllSharpConPermimp.pdf", width=8, height=5.4, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMnAllSharpConPermimp.pdf")
}


```

Selected metric

```{r}

allSharpVar <- "dSharpAurISO3PowAvgBin"

```

##### dTonal loudness and dtonality

###### Set variables

```{r}

iVars <- c(allVar, eventVar, ambVar, "dTonalECMAAvgMaxLR", "dTonalSHMInt05ExMaxLR", "dTonalSHMIntAvgMaxLR", "dTonalECMA05ExMaxLR", "dTonalAwSHMAvgMaxLR",	"dTonalAwSHM05ExMaxLR",	"dTonalAwSHMIntAvgMaxLR", 	"dTonalAwSHMInt05ExMaxLR", "dTonLdECMAPowAvgBin", "dTonLdECMA05ExBin", "dTonShpAurSHMPowAvgBin",
           "dTonShpAurSHM05ExBin", "PartTonLdSHMPowAvgBin", "UASTonalECMAAvgMaxLR", "UASTonalSHMInt05ExMaxLR", "UASTonalSHMIntAvgMaxLR", "UASTonalECMA05ExMaxLR", "UASTonalAwSHMAvgMaxLR",	"UASTonalAwSHM05ExMaxLR",	"UASTonalAwSHMIntAvgMaxLR", 	"UASTonalAwSHMInt05ExMaxLR", "UASTonLdECMAPowAvgBin", "UASTonLdECMA05ExBin", "UASTonalAurAvgMaxLR", "UASTonalAur05ExMaxLR", "UASTonalAur10ExMaxLR",
         "UASTonShpAurSHMPowAvgBin", "UASTonShpAurSHM05ExBin")
dVar <- "dAnnoyMean"

seeds <- c(561684, 104798, 1536, 48, 48561)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 251
mtry <- as.integer(length(iVars)/1.25)

```

###### Run model

Train preliminary model

```{r}
# Tonality with tonal loudness

nperm <- 5

resultsOutTonal1 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal1$OOB_RMSE
resultsOutTonal1$OOB_MAE
resultsOutTonal1$Rsquared
```

Train multiple seeds model

```{r}
# Tonality with tonal loudness

resultsOutTonal1 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal1$OOB_RMSE
resultsOutTonal1$OOB_MAE
resultsOutTonal1$Rsquared

```

```{r}
# store results
resdAnnoyMnFitAB['All tonal inc loud', 'RMSE'] <- resultsOutTonal1$OOB_RMSE
resdAnnoyMnFitAB['All tonal inc loud', 'MAE'] <- resultsOutTonal1$OOB_MAE
resdAnnoyMnFitAB['All tonal inc loud', 'Rsquared'] <- resultsOutTonal1$Rsquared
resdAnnoyMnPermImpAB$AllTonal1 <- resultsOutTonal1$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=6}

par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutTonal1.conimp <- arrange(resultsOutTonal1$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutTonal1.conimp) + geom_col(aes(x=factor(rownames(resultsOutTonal1.conimp), levels=rownames(resultsOutTonal1.conimp)), y=CondPermImp), fill=mycolours[3], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + ggtitle("All tonality inc. tonal loudness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 2.2))
pBar

if (saveplots){
  ggsave(filename="PtsABdAnnoyMnAllTonalLdConPermimp.svg", width=8, height=6, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMnAllTonalLdConPermimp.svg")
  
  ggsave(filename="PtsABdAnnoyMnAllTonalLdConPermimp.pdf", width=8, height=6, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMnAllTonalLdConPermimp.pdf")
}

```

Selected metric

```{r}

allTonLdVar <- "dTonLdECMAPowAvgBin"

```

##### dTonality without dtonal loudness

###### Set variables

```{r}

iVars <- c(allVar, eventVar, ambVar, "dTonalECMAAvgMaxLR", "dTonalSHMInt05ExMaxLR", "dTonalSHMIntAvgMaxLR", "dTonalECMA05ExMaxLR", "dTonalAwSHMAvgMaxLR",	"dTonalAwSHM05ExMaxLR",	"dTonalAwSHMIntAvgMaxLR", 	"dTonalAwSHMInt05ExMaxLR", "UASTonalECMAAvgMaxLR", "UASTonalSHMInt05ExMaxLR", "UASTonalSHMIntAvgMaxLR", "UASTonalECMA05ExMaxLR", "UASTonalAwSHMAvgMaxLR",	"UASTonalAwSHM05ExMaxLR",	"UASTonalAwSHMIntAvgMaxLR", "UASTonalAwSHMInt05ExMaxLR",	"UASTonalAurAvgMaxLR", "UASTonalAur05ExMaxLR", "UASTonalAur10ExMaxLR")
dVar <- "dAnnoyMean"

seeds <- c(410865, 2954, 70812, 203, 7984)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 501
mtry <- as.integer(length(iVars)/1.25)

```

###### Run model

Train preliminary model

```{r}
# Tonality

nperm <- 5

resultsOutTonal2 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
                           ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal2$OOB_RMSE
resultsOutTonal2$OOB_MAE
resultsOutTonal2$Rsquared

```

Train multiple seeds model

```{r}
# Tonality

resultsOutTonal2 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal2$OOB_RMSE
resultsOutTonal2$OOB_MAE
resultsOutTonal2$Rsquared

```


```{r}

# store results
resdAnnoyMnFitAB['All tonal no loud', 'RMSE'] <- resultsOutTonal2$OOB_RMSE
resdAnnoyMnFitAB['All tonal no loud', 'MAE'] <- resultsOutTonal2$OOB_MAE
resdAnnoyMnFitAB['All tonal no loud', 'Rsquared'] <- resultsOutTonal2$Rsquared
resdAnnoyMnPermImpAB$AllTonal2 <- resultsOutTonal2$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=5.8}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutTonal2.conimp <- arrange(resultsOutTonal2$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutTonal2.conimp) + geom_col(aes(x=factor(rownames(resultsOutTonal2.conimp), levels=rownames(resultsOutTonal2.conimp)), y=CondPermImp), fill=mycolours[3], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + ggtitle("All tonality w/o tonal loudness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 2.6))
pBar

if (saveplots){
  ggsave(filename="PtsABdAnnoyMnAllTonalConPermimp.svg", width=8, height=5.8, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMnAllTonalConPermimp.svg")
  
  ggsave(filename="PtsABAllAnnoyMndTonalConPermimp.pdf", width=8, height=5.8, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMnAllTonalConPermimp.pdf")
}


```

Selected metric

```{r}

allTonalVar <- "dTonalSHMIntAvgMaxLR"

```

##### dFluctuation strength

###### Set variables

```{r}

# Fluctuation strength
iVars <- c(allVar, eventVar, ambVar, "dFluctECMA10ExBin", "dFluctECMA05ExBin", "dFluctOV10ExMaxLR", "dFluctOV05ExMaxLR", "UASFluctOldSHM10ExBin", "UASFluctOldSHM05ExBin", "UASFluctECMA10ExBin", "UASFluctECMA05ExBin", "UASFluctFZ10ExMaxLR", "UASFluctFZ05ExMaxLR", "UASFluctOV10ExMaxLR", "UASFluctOV05ExMaxLR")
dVar <- "dAnnoyMean"

seeds <- c(418657, 84, 1630, 18659, 3687)

```


###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 4001
mtry <- as.integer(length(iVars)/1.5)

```

###### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutFluct <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutFluct$OOB_RMSE
resultsOutFluct$OOB_MAE
resultsOutFluct$Rsquared

```
Train multiple seeds model

```{r}

resultsOutFluct <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutFluct$OOB_RMSE
resultsOutFluct$OOB_MAE
resultsOutFluct$Rsquared

```


```{r}

# store results
resdAnnoyMnFitAB['All fluct', 'RMSE'] <- resultsOutFluct$OOB_RMSE
resdAnnoyMnFitAB['All fluct', 'MAE'] <- resultsOutFluct$OOB_MAE
resdAnnoyMnFitAB['All fluct', 'Rsquared'] <- resultsOutFluct$Rsquared
resdAnnoyMnPermImpAB$AllFluct <- resultsOutFluct$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=4}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutFluct.conimp <- arrange(resultsOutFluct$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutFluct.conimp) + geom_col(aes(x=factor(rownames(resultsOutFluct.conimp), levels=rownames(resultsOutFluct.conimp)), y=CondPermImp), fill=mycolours[4], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + ggtitle("All fluctuation strength") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdAnnoyMnAllFluctConPermimp.svg", width=8, height=4, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMnAllFluctConPermimp.svg")
  
  ggsave(filename="PtsABdAnnoyMnAllFluctConPermimp.pdf", width=8, height=4, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMnAllFluctConPermimp.pdf")
}

```

Selected metric

```{r}

allFluctVar <- "dFluctECMA10ExBin"

```


##### dRoughness

###### Set variables

```{r}

# Roughness
iVars <- c(allVar, eventVar, ambVar, "dRoughECMA10ExBin", "dRoughECMA05ExBin", "dRoughFZ10ExMaxLR", "dRoughFZ05ExMaxLR", "UASRoughECMA10ExBin", "UASRoughECMA05ExBin", "UASRoughFZ10ExMaxLR", "UASRoughFZ05ExMaxLR", "UASRoughDW10ExMaxLR", "UASRoughDW05ExMaxLR")
dVar <- "dAnnoyMean"

seeds <- c(69851, 85109, 410986, 1563, 896)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 1001
mtry <- as.integer(length(iVars)/1.25)

```

###### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutRough <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutRough$OOB_RMSE
resultsOutRough$OOB_MAE
resultsOutRough$Rsquared

```

Train multiple seeds model

```{r}

resultsOutRough <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutRough$OOB_RMSE
resultsOutRough$OOB_MAE
resultsOutRough$Rsquared

```

```{r}
# store results
resdAnnoyMnFitAB['All rough', 'RMSE'] <- resultsOutRough$OOB_RMSE
resdAnnoyMnFitAB['All rough', 'MAE'] <- resultsOutRough$OOB_MAE
resdAnnoyMnFitAB['All rough', 'Rsquared'] <- resultsOutRough$Rsquared
resdAnnoyMnPermImpAB$AllRough <- resultsOutRough$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=4}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutRough.conimp <- arrange(resultsOutRough$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutRough.conimp) + geom_col(aes(x=factor(rownames(resultsOutRough.conimp), levels=rownames(resultsOutRough.conimp)), y=CondPermImp), fill=mycolours[5], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + ggtitle("All roughness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdAnnoyMnAllRoughConPermimp.svg", width=8, height=4, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMnAllRoughConPermimp.svg")
  
  ggsave(filename="PtsABdAnnoyMnAllRoughConPermimp.pdf", width=8, height=4, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMnAllRoughConPermimp.pdf")
}


```

Selected metric

```{r}

allRoughVar <- "dRoughFZ05ExMaxLR"

```

##### dImpulsiveness

###### Set variables

```{r}
# Impulsiveness
iVars <- c(allVar, eventVar, ambVar, "dImpulsSHMAvgMaxLR", "dImpulsSHM05ExMaxLR", "dImpulsSHMPowAvgMaxLR",
           "dImpulsLoudWZAvgMaxLR", "dImpulsLoudWZ05ExMaxLR", "dImpulsLoudWZPowAvgMaxLR",
           "dImpulsLoudWECMAAvgBin", "dImpulsLoudWECMA05ExBin", "dImpulsLoudWECMAPowAvgBin", "UASImpulsSHMAvgMaxLR", "UASImpulsSHM05ExMaxLR", "UASImpulsSHMPowAvgMaxLR", "UASImpulsLoudWZAvgMaxLR", "UASImpulsLoudWZ05ExMaxLR", "UASImpulsLoudWZPowAvgMaxLR", "UASImpulsLoudWECMAAvgBin", "UASImpulsLoudWECMA05ExBin", "UASImpulsLoudWECMAPowAvgBin")
dVar <- "dAnnoyMean"

seeds <- c(418659, 7805, 38475, 65834, 1653)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 501
mtry <- as.integer(length(iVars)/1.25)

```

###### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutImpuls <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutImpuls$OOB_RMSE
resultsOutImpuls$OOB_MAE
resultsOutImpuls$Rsquared

```

Train multiple seeds model

```{r}

resultsOutImpuls <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutImpuls$OOB_RMSE
resultsOutImpuls$OOB_MAE
resultsOutImpuls$Rsquared

```

```{r}

# store results
resdAnnoyMnFitAB['All impuls', 'RMSE'] <- resultsOutImpuls$OOB_RMSE
resdAnnoyMnFitAB['All impuls', 'MAE'] <- resultsOutImpuls$OOB_MAE
resdAnnoyMnFitAB['All impuls', 'Rsquared'] <- resultsOutImpuls$Rsquared
resdAnnoyMnPermImpAB$AllImpuls <- resultsOutImpuls$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=5.6}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutImpuls.conimp <- arrange(resultsOutImpuls$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutImpuls.conimp) + geom_col(aes(x=factor(rownames(resultsOutImpuls.conimp), levels=rownames(resultsOutImpuls.conimp)), y=CondPermImp), fill=mycolours[6], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + ggtitle("All impulsiveness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdAnnoyMnAllImpulsConPermimp.svg", width=8, height=5.6, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMnAllImpulsConPermimp.svg")
  
  ggsave(filename="PtsABdAnnoyMnAllImpulsConPermimp.pdf", width=8, height=5.6, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMnAllImpulsConPermimp.pdf")
}

```

Selected metric

```{r}

allImpulsVar <- "dImpulsLoudWZAvgMaxLR"

```

#### dSQM and loudness comparison

Now the highest importance dSQMs are ranked against each other, controlling for loudness difference.

##### Include dtonal loudness

###### Set variables

```{r}

iVars <- c(allVar, eventVar, ambVar, allSharpVar, allTonLdVar, allFluctVar, allRoughVar, allImpulsVar)
dVar <- "dAnnoyMean"

seeds <- c(98465, 54163, 6541, 36485, 849675)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 501
mtry <- as.integer(length(iVars)/2)

```

###### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutSQMs1 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs1$OOB_RMSE
resultsOutSQMs1$OOB_MAE
resultsOutSQMs1$Rsquared

```

Train multiple seeds model

```{r}

resultsOutSQMs1 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs1$OOB_RMSE
resultsOutSQMs1$OOB_MAE
resultsOutSQMs1$Rsquared

```

```{r}

# store results
resdAnnoyMnFitAB['All SQMs inc tonal loud', 'RMSE'] <- resultsOutSQMs1$OOB_RMSE
resdAnnoyMnFitAB['All SQMs inc tonal loud', 'MAE'] <- resultsOutSQMs1$OOB_MAE
resdAnnoyMnFitAB['All SQMs inc tonal loud', 'Rsquared'] <- resultsOutSQMs1$Rsquared
resdAnnoyMnPermImpAB$AllSQMs1 <- resultsOutSQMs1$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=2.4}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutSQMs1.conimp <- arrange(resultsOutSQMs1$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutSQMs1.conimp) + geom_col(aes(x=factor(rownames(resultsOutSQMs1.conimp), levels=rownames(resultsOutSQMs1.conimp)), y=CondPermImp), fill=mycolours[7], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 2))
pBar

if (saveplots){
  ggsave(filename="PtsABdAnnoyMnAllSQMsTonLdConPermimp.svg", width=8, height=2.4, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMnAllSQMsTonLdConPermimp.svg")
  
  ggsave(filename="PtsABdAnnoyMnAllSQMsTonLdConPermimp.pdf", width=8, height=2.4, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMnAllSQMsTonLdConPermimp.pdf")
}

```

##### Exclude tonal loudness

###### Set variables

```{r}

iVars <- c(allVar, eventVar, ambVar, allSharpVar, allTonalVar, allFluctVar, allRoughVar, allImpulsVar)
dVar <- "dAnnoyMean"

seeds <- c(49865, 7852, 845961, 410583, 36748)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 2501
mtry <- as.integer(length(iVars)/1.6)

```


###### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutSQMs2 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs2$OOB_RMSE
resultsOutSQMs2$OOB_MAE
resultsOutSQMs2$Rsquared

```

Train multiple seeds model

```{r}

resultsOutSQMs2 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs2$OOB_RMSE
resultsOutSQMs2$OOB_MAE
resultsOutSQMs2$Rsquared

```

```{r}

# store results
resdAnnoyMnFitAB['All SQMs no tonal loud', 'RMSE'] <- resultsOutSQMs2$OOB_RMSE
resdAnnoyMnFitAB['All SQMs no tonal loud', 'MAE'] <- resultsOutSQMs2$OOB_MAE
resdAnnoyMnFitAB['All SQMs no tonal loud', 'Rsquared'] <- resultsOutSQMs2$Rsquared
resdAnnoyMnPermImpAB$AllSQMs2 <- resultsOutSQMs2$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=2.4}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutSQMs2.conimp <- arrange(resultsOutSQMs2$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutSQMs2.conimp) + geom_col(aes(x=factor(rownames(resultsOutSQMs2.conimp), levels=rownames(resultsOutSQMs2.conimp)), y=CondPermImp), fill=mycolours[7], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 2))
pBar

if (saveplots){
  ggsave(filename="PtsABdAnnoyMnAllSQMsNoTonLdConPermimp.svg", width=8, height=2.4, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMnAllSQMsNoTonLdConPermimp.svg")
  
  ggsave(filename="PtsABdAnnoyMnAllSQMsNoTonLdConPermimp.pdf", width=8, height=2.4, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMnAllSQMsNoTonLdConPermimp.pdf")
}

```

#### dPsychoacoustic annoyance metrics

##### Set variables

```{r}

iVars <- c(ambVar, "dPsychAnnoyWidmann", "dPsychAnnoyMore", "dPsychAnnoyDi", "dPsychAnnoyTorija", "dPsychAnnoyWillemsen", "dPsychAnnoyBoucher", "UASPsychAnnoyWidmann", "UASPsychAnnoyMore", "UASPsychAnnoyDi", "UASPsychAnnoyTorija", "UASPsychAnnoyWillemsen", "UASPsychAnnoyBoucher")
dVar <- "dAnnoyMean"

seeds <- c(47896643, 475, 654, 98987132, 5446)


```

##### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
              ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```


Selected hyperparameters

```{r}

ntree <- 4001
mtry <-  as.integer(length(iVars)/1.25)

```

##### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutPA <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutPA$OOB_RMSE
resultsOutPA$OOB_MAE
resultsOutPA$Rsquared

```

Train multiple seeds model

```{r}

resultsOutPA <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutPA$OOB_RMSE
resultsOutPA$OOB_MAE
resultsOutPA$Rsquared

```

```{r}

# store results
resdAnnoyMnFitAB['All Psychoacoustic annoyance', 'RMSE'] <- resultsOutPA$OOB_RMSE
resdAnnoyMnFitAB['All Psychoacoustic annoyance', 'MAE'] <- resultsOutPA$OOB_MAE
resdAnnoyMnFitAB['All Psychoacoustic annoyance', 'Rsquared'] <- resultsOutPA$Rsquared
resdAnnoyMnPermImpAB$AllPA <- resultsOutPA$conditional_permimp

```

##### Plot results

```{r, fig.width=8,fig.height=4}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutPA.conimp <- arrange(resultsOutPA$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutPA.conimp) + geom_col(aes(x=factor(rownames(resultsOutPA.conimp), levels=rownames(resultsOutPA.conimp)), y=CondPermImp), fill=mycolours[10], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (mean change in annoyance)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 1.8))
pBar

if (saveplots){
  ggsave(filename="PtsABdAnnoyMnAllPAConPermimp.svg", width=8, height=4, path=file.path(outFigPath, "svg"))
  unlink("PtsABdAnnoyMnAllPAConPermimp.svg")
  
  ggsave(filename="PtsABdAnnoyMnAllPAConPermimp.pdf", width=8, height=4, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdAnnoyMnAllPAConPermimp.pdf")
}

```

### Save the results outputs to file

```{r}

if (savedata){
  utils::write.csv(resdAnnoyMnFitAB, paste(outDataPath, "\\PtsABCRFdAnnoyMnOOBFit.csv", sep=""))
  ii <- 0
  temp = list()
  for (res in resdAnnoyMnPermImpAB){
    ii <- ii + 1
    temp[[ii]] <- as.data.frame(resdAnnoyMnPermImpAB[ii])
    names(temp[[ii]]) <- names(resdAnnoyMnPermImpAB[ii])
  }
  openxlsx::write.xlsx(temp, paste(outDataPath, "\\PtsABCRFdAnnoyMnConPermimp.xlsx",
                                   sep=""),
                       rowNames=TRUE)
}

```

## (Change to) High annoyance

### Initialise results output variables

```{r}
resdHiAnnoyFitAB <- data.frame(RMSE = numeric(),
                             MAE = numeric(),
                             Rsquared = numeric())
resdHiAnnoyPermImpAB <- list()

```

### Absolute variables

#### Set variables

```{r}

iVars <- names(stimDataNum)[which(names(stimDataNum) == 'UASEvents'):which(names(stimDataNum) == 'UASPsychAnnoyBoucher')]
iVars <- iVars[! iVars %in% c('SNRlevel', 'IntermitRatioC2MaxLR', 'IntermitRatioC3MaxLR', 'IntermitRatioC5MaxLR')]
dVar <- "dHighAnnoyPc"

seeds <- c(578312, 544, 84894, 54654, 153157)

```

#### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

if (saveplots){
  ggsave(filename="PtsABdHiAnnoyAbsVarsHyperTune.svg", width=12, height=4, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoyAbsVarsHyperTune.svg")

  ggsave(filename="PtsABdHiAnnoyAbsVarsHyperTune.pdf", width=12, height=4, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoyAbsVarsHyperTune.pdf")
}

```

Selected hyperparameters

```{r}

ntree <- 1501
mtry <- as.integer(length(iVars)/1.75)

```

#### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutAbs <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutAbs$OOB_RMSE
resultsOutAbs$OOB_MAE
resultsOutAbs$Rsquared

```
Train multiple seeds model

```{r}

resultsOutAbs <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutAbs$OOB_RMSE
resultsOutAbs$OOB_MAE
resultsOutAbs$Rsquared

```


```{r}

# store results
resdHiAnnoyFitAB['Abs vars', 'RMSE'] <- resultsOutAbs$OOB_RMSE
resdHiAnnoyFitAB['Abs vars', 'MAE'] <- resultsOutAbs$OOB_MAE
resdHiAnnoyFitAB['Abs vars', 'Rsquared'] <- resultsOutAbs$Rsquared
resdHiAnnoyPermImpAB$AbsVars <- resultsOutAbs$conditional_permimp

```

#### Plot results

```{r, fig.width=8,fig.height=14}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutAbs.conimp <- arrange(resultsOutAbs$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutAbs.conimp) + geom_col(aes(x=factor(rownames(resultsOutAbs.conimp), levels=rownames(resultsOutAbs.conimp)), y=CondPermImp), fill=mycolours[1], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) +
  coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdHiAnnoyAbsVarsConPermimp.svg", width=8, height=14, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoyAbsVarsConPermimp.svg")
  
  ggsave(filename="PtsABdHiAnnoyAbsVarsConPermimp.pdf", width=8, height=14, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoyAbsVarsConPermimp.pdf")
}
```

```{r, fig.width=8,fig.height=10}

# Plot only positive values
resultsOutAbs.conimpPtv <- resultsOutAbs.conimp |>
                                          rownames_to_column('Metric') |>
                                                filter_if(is.numeric, all_vars(. > 0)) |>
                                                      column_to_rownames('Metric')

pBar <- ggplot(resultsOutAbs.conimpPtv,) + geom_col(aes(x=factor(rownames(resultsOutAbs.conimpPtv), levels=rownames(resultsOutAbs.conimpPtv)), y=CondPermImp), fill=mycolours[1], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdHiAnnoyAbsVarsConPermimpPtv.svg", width=8, height=10, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoyAbsVarsConPermimpPtv.svg")
  
  ggsave(filename="PtsABdHiAnnoyAbsVarsConPermimpPtv.pdf", width=8, height=10, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoyAbsVarsConPermimpPtv.pdf")
}

```


```{r, fig.width=8,fig.height=4}

# Plot only values within 1% of the maximum
resultsOutAbs.conimp1pc <- resultsOutAbs.conimp |>
                                          rownames_to_column('Metric') |>
                                                filter_if(is.numeric, all_vars(. > max(resultsOutAbs.conimp)/100)) |>
                                                      column_to_rownames('Metric')

pBar <- ggplot(resultsOutAbs.conimp1pc,) + geom_col(aes(x=factor(rownames(resultsOutAbs.conimp1pc), levels=rownames(resultsOutAbs.conimp1pc)), y=CondPermImp), fill=mycolours[1], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdHiAnnoyAbsVarsConPermimp1pc.svg", width=8, height=4, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoyAbsVarsConPermimp1pc.svg")
  
  ggsave(filename="PtsABdHiAnnoyAbsVarsConPermimp1pc.pdf", width=8, height=4, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoyAbsVarsConPermimp1pc.pdf")
}

```

Selected metric

```{r}

absVar <- "UASLoudECMAPowAvgBin"

```

### SQM analysis

#### Individual SQMs

##### Sharpness

###### Set variables

```{r}

iVars <- c(absVar, eventVar, ambVar, "UASSharpAurISO3PowAvgBin", "UASSharpAurISO305ExBin", "UASSharpAurSHMPowAvgBin", "UASSharpAurSHM05ExBin", "UASSharpAurISO1PowAvgBin", "UASSharpAurISO105ExBin", "UASSharpvBISO1PowAvgBin", "UASSharpvBISO105ExBin", "UASSharpDINPowAvgBin", "UASSharpDIN05ExBin", "UASSharpAurISO1MedBin",
         "UASTonShpAurSHMPowAvgBin", "UASTonShpAurSHM05ExBin")
dVar <- "dHighAnnoyPc"

seeds <- c(7041, 905, 4984651, 6513213, 120651)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p


```

Selected hyperparameters

```{r}

ntree <- 1501
mtry <- as.integer(length(iVars)/2.25)

```

###### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutSharp <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSharp$OOB_RMSE
resultsOutSharp$OOB_MAE
resultsOutSharp$Rsquared

```

Train multiple seeds model

```{r}

resultsOutSharp <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSharp$OOB_RMSE
resultsOutSharp$OOB_MAE
resultsOutSharp$Rsquared

```


```{r}

# store results
resdHiAnnoyFitAB['Abs sharp', 'RMSE'] <- resultsOutSharp$OOB_RMSE
resdHiAnnoyFitAB['Abs sharp', 'MAE'] <- resultsOutSharp$OOB_MAE
resdHiAnnoyFitAB['Abs sharp', 'Rsquared'] <- resultsOutSharp$Rsquared
resdHiAnnoyPermImpAB$AbsSharp <- resultsOutSharp$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=4.9}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutSharp.conimp <- arrange(resultsOutSharp$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutSharp.conimp) + geom_col(aes(x=factor(rownames(resultsOutSharp.conimp), levels=rownames(resultsOutSharp.conimp)), y=CondPermImp), fill=mycolours[2], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + ggtitle("Sharpness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdHiAnnoySharpConPermimp.svg", width=8, height=4.9, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoySharpConPermimp.svg")
  
  ggsave(filename="PtsABdHiAnnoySharpConPermimp.pdf", width=8, height=4.9, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoySharpConPermimp.pdf")
}

```

Selected metric

```{r}

sharpVar <- "UASSharpAurISO3PowAvgBin"

```


##### Tonal loudness and tonality

###### Set variables

```{r}

iVars <- c(absVar, eventVar, ambVar, "UASTonalECMAAvgMaxLR", "UASTonalSHMInt05ExMaxLR", "UASTonalSHMIntAvgMaxLR", "UASTonalECMA05ExMaxLR", "UASTonalAwSHMAvgMaxLR",	"UASTonalAwSHM05ExMaxLR",	"UASTonalAwSHMIntAvgMaxLR", 	"UASTonalAwSHMInt05ExMaxLR", "UASTonLdECMAPowAvgBin", "UASTonLdECMA05ExBin", "UASTonalAurAvgMaxLR", "UASTonalAur05ExMaxLR", "UASTonalAur10ExMaxLR",
         "UASTonShpAurSHMPowAvgBin", "UASTonShpAurSHM05ExBin")
dVar <- "dHighAnnoyPc"

seeds <- c(540, 104798, 456464, 87331, 94564)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 251
mtry <- as.integer(length(iVars)/1.5)

```

###### Run model

Train preliminary model

```{r}
# Tonality with tonal loudness

nperm <- 5

resultsOutTonal1 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal1$OOB_RMSE
resultsOutTonal1$OOB_MAE
resultsOutTonal1$Rsquared
```

Train multiple seeds model

```{r}
# Tonality with tonal loudness

resultsOutTonal1 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal1$OOB_RMSE
resultsOutTonal1$OOB_MAE
resultsOutTonal1$Rsquared

```

```{r}
# store results
resdHiAnnoyFitAB['Abs tonal inc loud', 'RMSE'] <- resultsOutTonal1$OOB_RMSE
resdHiAnnoyFitAB['Abs tonal inc loud', 'MAE'] <- resultsOutTonal1$OOB_MAE
resdHiAnnoyFitAB['Abs tonal inc loud', 'Rsquared'] <- resultsOutTonal1$Rsquared
resdHiAnnoyPermImpAB$AbsTonal1 <- resultsOutTonal1$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=4.4}

par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutTonal1.conimp <- arrange(resultsOutTonal1$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutTonal1.conimp) + geom_col(aes(x=factor(rownames(resultsOutTonal1.conimp), levels=rownames(resultsOutTonal1.conimp)), y=CondPermImp), fill=mycolours[3], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + ggtitle("Tonality inc. tonal loudness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 110))
pBar

if (saveplots){
  ggsave(filename="PtsABdHiAnnoyTonalLdConPermimp.svg", width=8, height=4.4, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoyTonalLdConPermimp.svg")
  
  ggsave(filename="PtsABdHiAnnoyTonalLdConPermimp.pdf", width=8, height=4.4, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoyTonalLdConPermimp.pdf")
}

```

Selected metric

```{r}

tonLdVar <- "UASTonLdECMAPowAvgBin"

```


##### Tonality without tonal loudness

###### Set variables

```{r}

iVars <- c(absVar, eventVar, ambVar, "UASTonalECMAAvgMaxLR", "UASTonalSHMInt05ExMaxLR", "UASTonalSHMIntAvgMaxLR", "UASTonalECMA05ExMaxLR", "UASTonalAwSHMAvgMaxLR",	"UASTonalAwSHM05ExMaxLR",	"UASTonalAwSHMIntAvgMaxLR", "UASTonalAwSHMInt05ExMaxLR",	"UASTonalAurAvgMaxLR", "UASTonalAur05ExMaxLR", "UASTonalAur10ExMaxLR")
dVar <- "dHighAnnoyPc"

seeds <- c(156089, 5860, 10528, 89541, 4685146)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 251
mtry <- as.integer(length(iVars)/1.25)

```


###### Run model

Train preliminary model

```{r}
# Tonality

nperm <- 5

resultsOutTonal2 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
                           ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm,
                           minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal2$OOB_RMSE
resultsOutTonal2$OOB_MAE
resultsOutTonal2$Rsquared

```

Train multiple seeds model

```{r}
# Tonality

resultsOutTonal2 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal2$OOB_RMSE
resultsOutTonal2$OOB_MAE
resultsOutTonal2$Rsquared

```


```{r}

# store results
resdHiAnnoyFitAB['Abs tonal no loud', 'RMSE'] <- resultsOutTonal2$OOB_RMSE
resdHiAnnoyFitAB['Abs tonal no loud', 'MAE'] <- resultsOutTonal2$OOB_MAE
resdHiAnnoyFitAB['Abs tonal no loud', 'Rsquared'] <- resultsOutTonal2$Rsquared
resdHiAnnoyPermImpAB$AbsTonal2 <- resultsOutTonal2$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=3.8}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutTonal2.conimp <- arrange(resultsOutTonal2$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutTonal2.conimp) + geom_col(aes(x=factor(rownames(resultsOutTonal2.conimp), levels=rownames(resultsOutTonal2.conimp)), y=CondPermImp), fill=mycolours[3], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + ggtitle("Tonality w/o tonal loudness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 110))
pBar

if (saveplots){
  ggsave(filename="PtsABdHiAnnoyTonalConPermimp.svg", width=8, height=3.8, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoyTonalConPermimp.svg")
  
  ggsave(filename="PtsABdHiAnnoyTonalConPermimp.pdf", width=8, height=3.8, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoyTonalConPermimp.pdf")
}

```

Selected metric

```{r}

tonalVar <- "UASTonalAwSHMInt05ExMaxLR"

```

##### Fluctuation strength

###### Set variables

```{r}

# Fluctuation strength
iVars <- c(absVar, eventVar, ambVar, "UASFluctOldSHM10ExBin", "UASFluctOldSHM05ExBin", "UASFluctECMA10ExBin", "UASFluctECMA05ExBin", "UASFluctFZ10ExMaxLR", "UASFluctFZ05ExMaxLR", "UASFluctOV10ExMaxLR", "UASFluctOV05ExMaxLR")
dVar <- "dHighAnnoyPc"

seeds <- c(25107, 546098, 195, 5937, 102658)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 251
mtry <- as.integer(length(iVars)/1.25)

```

###### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutFluct <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
                          ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres,
                          nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutFluct$OOB_RMSE
resultsOutFluct$OOB_MAE
resultsOutFluct$Rsquared

```
Train multiple seeds model

```{r}

resultsOutFluct <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutFluct$OOB_RMSE
resultsOutFluct$OOB_MAE
resultsOutFluct$Rsquared

```


```{r}

# store results
resdHiAnnoyFitAB['Abs fluct', 'RMSE'] <- resultsOutFluct$OOB_RMSE
resdHiAnnoyFitAB['Abs fluct', 'MAE'] <- resultsOutFluct$OOB_MAE
resdHiAnnoyFitAB['Abs fluct', 'Rsquared'] <- resultsOutFluct$Rsquared
resdHiAnnoyPermImpAB$AbsFluct <- resultsOutFluct$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=2.9}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutFluct.conimp <- arrange(resultsOutFluct$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutFluct.conimp) + geom_col(aes(x=factor(rownames(resultsOutFluct.conimp), levels=rownames(resultsOutFluct.conimp)), y=CondPermImp), fill=mycolours[4], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + ggtitle("Fluctuation strength") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdHiAnnoyFluctConPermimp.svg", width=8, height=2.9, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoyFluctConPermimp.svg")
  
  ggsave(filename="PtsABdHiAnnoyFluctConPermimp.pdf", width=8, height=2.9, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoyFluctConPermimp.pdf")
}

```

Selected metric

```{r}

fluctVar <- "UASFluctECMA10ExBin"

```

##### Roughness

###### Set variables

```{r}

# Roughness
iVars <- c(absVar, eventVar, ambVar, "UASRoughECMA10ExBin", "UASRoughECMA05ExBin", "UASRoughFZ10ExMaxLR", "UASRoughFZ05ExMaxLR", "UASRoughDW10ExMaxLR", "UASRoughDW05ExMaxLR")
dVar <- "dHighAnnoyPc"

seeds <- c(4701, 52187, 16589, 65217, 16893)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 4001
mtry <- as.integer(length(iVars)/1.5)

```


###### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutRough <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutRough$OOB_RMSE
resultsOutRough$OOB_MAE
resultsOutRough$Rsquared

```

Train multiple seeds model

```{r}

resultsOutRough <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutRough$OOB_RMSE
resultsOutRough$OOB_MAE
resultsOutRough$Rsquared

```

```{r}
# store results
resdHiAnnoyFitAB['Abs rough', 'RMSE'] <- resultsOutRough$OOB_RMSE
resdHiAnnoyFitAB['Abs rough', 'MAE'] <- resultsOutRough$OOB_MAE
resdHiAnnoyFitAB['Abs rough', 'Rsquared'] <- resultsOutRough$Rsquared
resdHiAnnoyPermImpAB$AbsRough <- resultsOutRough$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=2.9}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutRough.conimp <- arrange(resultsOutRough$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutRough.conimp) + geom_col(aes(x=factor(rownames(resultsOutRough.conimp), levels=rownames(resultsOutRough.conimp)), y=CondPermImp), fill=mycolours[5], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + ggtitle("Roughness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdHiAnnoyRoughConPermimp.svg", width=8, height=2.9, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoyRoughConPermimp.svg")
  
  ggsave(filename="PtsABdHiAnnoyRoughConPermimp.pdf", width=8, height=2.9, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoyRoughConPermimp.pdf")
}


```

Selected metric

```{r}

roughVar <- "UASRoughFZ05ExMaxLR"

```


##### Impulsiveness

###### Set variables

```{r}
# Impulsiveness
iVars <- c(absVar, eventVar, ambVar, "UASImpulsSHMAvgMaxLR", "UASImpulsSHM05ExMaxLR", "UASImpulsSHMPowAvgMaxLR", "UASImpulsLoudWZAvgMaxLR", "UASImpulsLoudWZ05ExMaxLR", "UASImpulsLoudWZPowAvgMaxLR", "UASImpulsLoudWECMAAvgBin", "UASImpulsLoudWECMA05ExBin", "UASImpulsLoudWECMAPowAvgBin")
dVar <- "dHighAnnoyPc"

seeds <- c(8495, 59867, 5416, 9843, 86)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 501
mtry <- as.integer(length(iVars)/1.5)

```


###### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutImpuls <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutImpuls$OOB_RMSE
resultsOutImpuls$OOB_MAE
resultsOutImpuls$Rsquared

```

Train multiple seeds model

```{r}

resultsOutImpuls <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutImpuls$OOB_RMSE
resultsOutImpuls$OOB_MAE
resultsOutImpuls$Rsquared

```

```{r}

# store results
resdHiAnnoyFitAB['Abs impuls', 'RMSE'] <- resultsOutImpuls$OOB_RMSE
resdHiAnnoyFitAB['Abs impuls', 'MAE'] <- resultsOutImpuls$OOB_MAE
resdHiAnnoyFitAB['Abs impuls', 'Rsquared'] <- resultsOutImpuls$Rsquared
resdHiAnnoyPermImpAB$AbsImpuls <- resultsOutImpuls$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=4}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutImpuls.conimp <- arrange(resultsOutImpuls$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutImpuls.conimp) + geom_col(aes(x=factor(rownames(resultsOutImpuls.conimp), levels=rownames(resultsOutImpuls.conimp)), y=CondPermImp), fill=mycolours[6], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + ggtitle("Impulsiveness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdHiAnnoyImpulsConPermimp.svg", width=8, height=4, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoyImpulsConPermimp.svg")
  
  ggsave(filename="PtsABdHiAnnoyImpulsConPermimp.pdf", width=8, height=4, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoyImpulsConPermimp.pdf")
}

```

Selected metric

```{r}

impulsVar <- "UASImpulsLoudWZAvgMaxLR"

```

#### SQM and loudness comparison

Now the highest importance SQMs are ranked against each other, controlling for UAS loudness and ambient LAeq.

##### Include tonal loudness

###### Set variables

```{r}

iVars <- c(absVar, eventVar, ambVar, sharpVar, tonLdVar, fluctVar, roughVar, impulsVar)
dVar <- "dHighAnnoyPc"

seeds <- c(70498, 4, 14986, 453, 864)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 1501
mtry <- 3

```


###### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutSQMs1 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs1$OOB_RMSE
resultsOutSQMs1$OOB_MAE
resultsOutSQMs1$Rsquared

```

Train multiple seeds model

```{r}

resultsOutSQMs1 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs1$OOB_RMSE
resultsOutSQMs1$OOB_MAE
resultsOutSQMs1$Rsquared

```

```{r}

# store results
resdHiAnnoyFitAB['Abs SQMs inc tonal loud', 'RMSE'] <- resultsOutSQMs1$OOB_RMSE
resdHiAnnoyFitAB['Abs SQMs inc tonal loud', 'MAE'] <- resultsOutSQMs1$OOB_MAE
resdHiAnnoyFitAB['Abs SQMs inc tonal loud', 'Rsquared'] <- resultsOutSQMs1$Rsquared
resdHiAnnoyPermImpAB$AbsSQMs1 <- resultsOutSQMs1$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=2.4}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutSQMs1.conimp <- arrange(resultsOutSQMs1$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutSQMs1.conimp) + geom_col(aes(x=factor(rownames(resultsOutSQMs1.conimp), levels=rownames(resultsOutSQMs1.conimp)), y=CondPermImp), fill=mycolours[7], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 30))
pBar

if (saveplots){
  ggsave(filename="PtsABdHiAnnoyAbsSQMsTonLdConPermimp.svg", width=8, height=2.4, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoyAbsSQMsTonLdConPermimp.svg")
  
  ggsave(filename="PtsABdHiAnnoyAbsSQMsTonLdConPermimp.pdf", width=8, height=2.4, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoyAbsSQMsTonLdConPermimp.pdf")
}

```

##### Exclude tonal loudness

###### Set variables

```{r}

iVars <- c(absVar, eventVar, ambVar, sharpVar, tonalVar, fluctVar, roughVar, impulsVar)
dVar <- "dHighAnnoyPc"

seeds <- c(546, 57203, 270835, 60592, 8094)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 4001
mtry <- 3

```


###### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutSQMs2 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs2$OOB_RMSE
resultsOutSQMs2$OOB_MAE
resultsOutSQMs2$Rsquared

```

Train multiple seeds model

```{r}

resultsOutSQMs2 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs2$OOB_RMSE
resultsOutSQMs2$OOB_MAE
resultsOutSQMs2$Rsquared

```

```{r}

# store results
resdHiAnnoyFitAB['Abs SQMs no tonal loud', 'RMSE'] <- resultsOutSQMs2$OOB_RMSE
resdHiAnnoyFitAB['Abs SQMs no tonal loud', 'MAE'] <- resultsOutSQMs2$OOB_MAE
resdHiAnnoyFitAB['Abs SQMs no tonal loud', 'Rsquared'] <- resultsOutSQMs2$Rsquared
resdHiAnnoyPermImpAB$AbsSQMs2 <- resultsOutSQMs2$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=2.4}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutSQMs2.conimp <- arrange(resultsOutSQMs2$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutSQMs2.conimp) + geom_col(aes(x=factor(rownames(resultsOutSQMs2.conimp), levels=rownames(resultsOutSQMs2.conimp)), y=CondPermImp), fill=mycolours[7], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 30))
pBar

if (saveplots){
  ggsave(filename="PtsABdHiAnnoyAbsSQMsNoTonLdConPermimp.svg", width=8, height=2.4, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoyAbsSQMsNoTonLdConPermimp.svg")
  
  ggsave(filename="PtsABdHiAnnoyAbsSQMsNoTonLdConPermimp.pdf", width=8, height=2.4, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoyAbsSQMsNoTonLdConPermimp.pdf")
}

```

#### Psychoacoustic annoyance metrics

##### Set variables

```{r}

iVars <- c(ambVar, "UASPsychAnnoyWidmann", "UASPsychAnnoyMore", "UASPsychAnnoyDi", "UASPsychAnnoyTorija", "UASPsychAnnoyWillemsen", "UASPsychAnnoyBoucher")
dVar <- "dHighAnnoyPc"

seeds <- c(48651, 45, 785123, 65, 5163)


```

##### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```


Selected hyperparameters

```{r}

ntree <- 4001
mtry <- 4

```

##### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutPA <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutPA$OOB_RMSE
resultsOutPA$OOB_MAE
resultsOutPA$Rsquared

```

Train multiple seeds model

```{r}

resultsOutPA <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutPA$OOB_RMSE
resultsOutPA$OOB_MAE
resultsOutPA$Rsquared

```

```{r}

# store results
resdHiAnnoyFitAB['Psychoacoustic annoyance', 'RMSE'] <- resultsOutPA$OOB_RMSE
resdHiAnnoyFitAB['Psychoacoustic annoyance', 'MAE'] <- resultsOutPA$OOB_MAE
resdHiAnnoyFitAB['Psychoacoustic annoyance', 'Rsquared'] <- resultsOutPA$Rsquared
resdHiAnnoyPermImpAB$AbsPA <- resultsOutPA$conditional_permimp

```

##### Plot results

```{r, fig.width=8,fig.height=2.4}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutPA.conimp <- arrange(resultsOutPA$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutPA.conimp) + geom_col(aes(x=factor(rownames(resultsOutPA.conimp), levels=rownames(resultsOutPA.conimp)), y=CondPermImp), fill=mycolours[10], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 60))
pBar

if (saveplots){
  ggsave(filename="PtsABdHiAnnoyAbsPAConPermimp.svg", width=8, height=2.4, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoyAbsPAConPermimp.svg")
  
  ggsave(filename="PtsABdHiAnnoyAbsPAConPermimp.pdf", width=8, height=2.4, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoyAbsPAConPermimp.pdf")
}

```

### All variables (absolute and difference)

#### Set variables

```{r}

iVars <- names(stimDataNum)[which(names(stimDataNum) == 'UASEvents'):which(names(stimDataNum) == 'UASPsychAnnoyBoucher')]
iVars <- iVars[! iVars %in% 'SNRlevel']
iVars <- c(iVars,
           names(stimDataNum)[which(colnames(stimDataNum)=='LAeqLAF90diff'):
                               which(colnames(stimDataNum)=='dPsychAnnoyBoucher')], 'SNRlevel')
dVar <- "dHighAnnoyPc"

seeds <- c(2, 312, 1897, 465978, 821659)

```

#### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
              ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 1501
mtry <- as.integer(length(iVars)/3.5)

```

#### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutAbsDiffs <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutAbsDiffs$OOB_RMSE
resultsOutAbsDiffs$OOB_MAE
resultsOutAbsDiffs$Rsquared

```

Train multiple seeds model

```{r}

resultsOutAbsDiffs <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutAbsDiffs$OOB_RMSE
resultsOutAbsDiffs$OOB_MAE
resultsOutAbsDiffs$Rsquared
```

```{r}
# store results
resdHiAnnoyFitAB['All vars', 'RMSE'] <- resultsOutAbsDiffs$OOB_RMSE
resdHiAnnoyFitAB['All vars', 'MAE'] <- resultsOutAbsDiffs$OOB_MAE
resdHiAnnoyFitAB['All vars', 'Rsquared'] <- resultsOutAbsDiffs$Rsquared
resdHiAnnoyPermImpAB$AllVars <- resultsOutAbsDiffs$conditional_permimp

```

#### Plot results

```{r, fig.width=8,fig.height=26}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutAbsDiffs.conimp <- arrange(resultsOutAbsDiffs$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutAbsDiffs.conimp) + geom_col(aes(x=factor(rownames(resultsOutAbsDiffs.conimp), levels=rownames(resultsOutAbsDiffs.conimp)), y=CondPermImp), fill=mycolours[9], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdHiAnnoyAllVarsConPermimp.svg", width=8, height=26, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoyAllVarsConPermimp.svg")

  ggsave(filename="PtsABdHiAnnoyAllVarsConPermimp.pdf", width=8, height=26, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoyAllVarsConPermimp.pdf")
}

```

```{r, fig.width=8,fig.height=22}

# Plot only positive values

resultsOutAbsDiffs.conimpPtv <- resultsOutAbsDiffs.conimp |>
                                          rownames_to_column('Metric') |>
                                                filter_if(is.numeric, all_vars(. > 0)) |>
                                                      column_to_rownames('Metric')

pBar <- ggplot(resultsOutAbsDiffs.conimpPtv) + geom_col(aes(x=factor(rownames(resultsOutAbsDiffs.conimpPtv), levels=rownames(resultsOutAbsDiffs.conimpPtv)), y=CondPermImp), fill=mycolours[9], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdHiAnnoyAllVarsConPermimpPtv.svg", width=8, height=22, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoyAllVarsConPermimp.svg")
  
  ggsave(filename="PtsABdHiAnnoyAllVarsConPermimpPtv.pdf", width=8, height=22, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoyAllVarsConPermimp.pdf")
}

```


```{r, fig.width=8,fig.height=7}

# Plot only values within 1% of the maximum

resultsOutAbsDiffs.conimp1pc <- resultsOutAbsDiffs.conimp |>
                                          rownames_to_column('Metric') |>
                                                filter_if(is.numeric, all_vars(. > max(resultsOutAbsDiffs.conimp)/100)) |>
                                                      column_to_rownames('Metric')

pBar <- ggplot(resultsOutAbsDiffs.conimp1pc) + geom_col(aes(x=factor(rownames(resultsOutAbsDiffs.conimp1pc), levels=rownames(resultsOutAbsDiffs.conimp1pc)), y=CondPermImp), fill=mycolours[9], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdHiAnnoyAllVarsConPermimp1pc.svg", width=8, height=7, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoyAllVarsConPermimp1pc.svg")
  
  ggsave(filename="PtsABdHiAnnoyAllVarsConPermimp1pc.pdf", width=8, height=7, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoyAllVarsConPermimp1pc.pdf")
}

```

Selected metric

```{r}

allVar <- "UASLoudECMAPowAvgBin"

```

### dSQM analysis

#### Individual SQMs

##### dSharpness

###### Set variables

```{r}

iVars <- c(allVar, eventVar, ambVar, "dSharpAurISO3PowAvgBin", "dSharpAurISO305ExBin", "dSharpAurSHMPowAvgBin", "dSharpAurSHM05ExBin", "dTonShpAurSHMPowAvgBin", "dTonShpAurSHM05ExBin", "PartTonShpAurSHMPowAvgBin",
           "PartTonShpAurSHM05ExBin", "UASSharpAurISO3PowAvgBin", "UASSharpAurISO305ExBin", "UASSharpAurSHMPowAvgBin", "UASSharpAurSHM05ExBin", "UASSharpAurISO1PowAvgBin", "UASSharpAurISO105ExBin", "UASSharpvBISO1PowAvgBin", "UASSharpvBISO105ExBin", "UASSharpDINPowAvgBin", "UASSharpDIN05ExBin", "UASSharpAurISO1MedBin",
         "UASTonShpAurSHMPowAvgBin", "UASTonShpAurSHM05ExBin")
dVar <- "dHighAnnoyPc"

seeds <- c(84194, 905, 64815, 928054, 625091, 582031)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 251
mtry <- as.integer(length(iVars)/2.25)

```


###### Run model

Train preliminary model

```{r}

nperm <- 10

resultsOutSharp <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSharp$OOB_RMSE
resultsOutSharp$OOB_MAE
resultsOutSharp$Rsquared

```
Train multiple seeds model

```{r}

resultsOutSharp <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSharp$OOB_RMSE
resultsOutSharp$OOB_MAE
resultsOutSharp$Rsquared

```


```{r}
# store results
resdHiAnnoyFitAB['All sharp', 'RMSE'] <- resultsOutSharp$OOB_RMSE
resdHiAnnoyFitAB['All sharp', 'MAE'] <- resultsOutSharp$OOB_MAE
resdHiAnnoyFitAB['All sharp', 'Rsquared'] <- resultsOutSharp$Rsquared
resdHiAnnoyPermImpAB$AllSharp <- resultsOutSharp$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=5}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutSharp.conimp <- arrange(resultsOutSharp$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutSharp.conimp) + geom_col(aes(x=factor(rownames(resultsOutSharp.conimp), levels=rownames(resultsOutSharp.conimp)), y=CondPermImp), fill=mycolours[2], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + ggtitle("All sharpness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdHiAnnoyAllSharpConPermimp.svg", width=8, height=5, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoyAllSharpConPermimp.svg")
  
  ggsave(filename="PtsABdHiAnnoyAllSharpConPermimp.pdf", width=8, height=5, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoyAllSharpConPermimp.pdf")
}


```

Selected metric

```{r}

allSharpVar <- "dSharpAurSHMPowAvgBin"

```


##### dTonal loudness and dtonality

###### Set variables

```{r}

iVars <- c(allVar, eventVar, ambVar, "dTonalECMAAvgMaxLR", "dTonalSHMInt05ExMaxLR", "dTonalSHMIntAvgMaxLR", "dTonalECMA05ExMaxLR", "dTonalAwSHMAvgMaxLR",	"dTonalAwSHM05ExMaxLR",	"dTonalAwSHMIntAvgMaxLR", 	"dTonalAwSHMInt05ExMaxLR", "dTonLdECMAPowAvgBin", "dTonLdECMA05ExBin", "dTonShpAurSHMPowAvgBin",
           "dTonShpAurSHM05ExBin", "PartTonLdSHMPowAvgBin", "UASTonalECMAAvgMaxLR", "UASTonalSHMInt05ExMaxLR", "UASTonalSHMIntAvgMaxLR", "UASTonalECMA05ExMaxLR", "UASTonalAwSHMAvgMaxLR",	"UASTonalAwSHM05ExMaxLR",	"UASTonalAwSHMIntAvgMaxLR", 	"UASTonalAwSHMInt05ExMaxLR", "UASTonLdECMAPowAvgBin", "UASTonLdECMA05ExBin", "UASTonalAurAvgMaxLR", "UASTonalAur05ExMaxLR", "UASTonalAur10ExMaxLR",
         "UASTonShpAurSHMPowAvgBin", "UASTonShpAurSHM05ExBin")
dVar <- "dHighAnnoyPc"

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

seeds <- c(561684, 104798, 1536, 48, 48561)

```
Selected hyperparameters

```{r}

ntree <- 501
mtry <- as.integer(length(iVars)/2.25)

```

###### Run model

Train preliminary model

```{r}
# Tonality with tonal loudness

nperm <- 5

resultsOutTonal1 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal1$OOB_RMSE
resultsOutTonal1$OOB_MAE
resultsOutTonal1$Rsquared
```

Train multiple seeds model

```{r}
# Tonality with tonal loudness

resultsOutTonal1 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal1$OOB_RMSE
resultsOutTonal1$OOB_MAE
resultsOutTonal1$Rsquared

```

```{r}
# store results
resdHiAnnoyFitAB['All tonal inc loud', 'RMSE'] <- resultsOutTonal1$OOB_RMSE
resdHiAnnoyFitAB['All tonal inc loud', 'MAE'] <- resultsOutTonal1$OOB_MAE
resdHiAnnoyFitAB['All tonal inc loud', 'Rsquared'] <- resultsOutTonal1$Rsquared
resdHiAnnoyPermImpAB$AllTonal1 <- resultsOutTonal1$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=6}

par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutTonal1.conimp <- arrange(resultsOutTonal1$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutTonal1.conimp) + geom_col(aes(x=factor(rownames(resultsOutTonal1.conimp), levels=rownames(resultsOutTonal1.conimp)), y=CondPermImp), fill=mycolours[3], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + ggtitle("All tonality inc. tonal loudness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 50))
pBar

if (saveplots){
  ggsave(filename="PtsABdHiAnnoyAllTonalLdConPermimp.svg", width=8, height=6, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoyAllTonalLdConPermimp.svg")
  
  ggsave(filename="PtsABdHiAnnoyAllTonalLdConPermimp.pdf", width=8, height=6, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoyAllTonalLdConPermimp.pdf")
}

```

Selected metric

```{r}

allTonLdVar <- "UASTonLdECMAPowAvgBin"

```

##### dTonality without dtonal loudness

###### Set variables

```{r}

iVars <- c(allVar, eventVar, ambVar, "dTonalECMAAvgMaxLR", "dTonalSHMInt05ExMaxLR", "dTonalSHMIntAvgMaxLR", "dTonalECMA05ExMaxLR", "dTonalAwSHMAvgMaxLR",	"dTonalAwSHM05ExMaxLR",	"dTonalAwSHMIntAvgMaxLR", 	"dTonalAwSHMInt05ExMaxLR", "UASTonalECMAAvgMaxLR", "UASTonalSHMInt05ExMaxLR", "UASTonalSHMIntAvgMaxLR", "UASTonalECMA05ExMaxLR", "UASTonalAwSHMAvgMaxLR",	"UASTonalAwSHM05ExMaxLR",	"UASTonalAwSHMIntAvgMaxLR", "UASTonalAwSHMInt05ExMaxLR",	"UASTonalAurAvgMaxLR", "UASTonalAur05ExMaxLR", "UASTonalAur10ExMaxLR")
dVar <- "dHighAnnoyPc"

seeds <- c(410865, 2954, 70812, 203, 7984)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 501
mtry <- as.integer(length(iVars)/1.25)

```


###### Run model

Train preliminary model

```{r}
# Tonality

nperm <- 5

resultsOutTonal2 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal2$OOB_RMSE
resultsOutTonal2$OOB_MAE
resultsOutTonal2$Rsquared

```

Train multiple seeds model

```{r}
# Tonality

resultsOutTonal2 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutTonal2$OOB_RMSE
resultsOutTonal2$OOB_MAE
resultsOutTonal2$Rsquared

```


```{r}

# store results
resdHiAnnoyFitAB['All tonal no loud', 'RMSE'] <- resultsOutTonal2$OOB_RMSE
resdHiAnnoyFitAB['All tonal no loud', 'MAE'] <- resultsOutTonal2$OOB_MAE
resdHiAnnoyFitAB['All tonal no loud', 'Rsquared'] <- resultsOutTonal2$Rsquared
resdHiAnnoyPermImpAB$AllTonal2 <- resultsOutTonal2$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=4.8}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutTonal2.conimp <- arrange(resultsOutTonal2$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutTonal2.conimp) + geom_col(aes(x=factor(rownames(resultsOutTonal2.conimp), levels=rownames(resultsOutTonal2.conimp)), y=CondPermImp), fill=mycolours[3], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + ggtitle("All tonality w/o tonal loudness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 100))
pBar

if (saveplots){
  ggsave(filename="PtsABdHiAnnoyAllTonalConPermimp.svg", width=8, height=4.8, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoyAllTonalConPermimp.svg")
  
  ggsave(filename="PtsABdHiAnnoyAllTonalConPermimp.pdf", width=8, height=4.8, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoyAllTonalConPermimp.pdf")
}


```

Selected metric

```{r}

allTonalVar <- "UASTonalAwSHMInt05ExMaxLR"

```

##### dFluctuation strength

###### Set variables

```{r}

# Fluctuation strength
iVars <- c(allVar, eventVar, ambVar, "dFluctECMA10ExBin", "dFluctECMA05ExBin", "dFluctOV10ExMaxLR", "dFluctOV05ExMaxLR", "UASFluctOldSHM10ExBin", "UASFluctOldSHM05ExBin", "UASFluctECMA10ExBin", "UASFluctECMA05ExBin", "UASFluctFZ10ExMaxLR", "UASFluctFZ05ExMaxLR", "UASFluctOV10ExMaxLR", "UASFluctOV05ExMaxLR")
dVar <- "dHighAnnoyPc"

seeds <- c(418657, 84, 1630, 18659, 3687)

```


###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 251
mtry <- as.integer(length(iVars)/1.25)

```

###### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutFluct <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutFluct$OOB_RMSE
resultsOutFluct$OOB_MAE
resultsOutFluct$Rsquared

```

Train multiple seeds model

```{r}

resultsOutFluct <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutFluct$OOB_RMSE
resultsOutFluct$OOB_MAE
resultsOutFluct$Rsquared

```


```{r}

# store results
resdHiAnnoyFitAB['All fluct', 'RMSE'] <- resultsOutFluct$OOB_RMSE
resdHiAnnoyFitAB['All fluct', 'MAE'] <- resultsOutFluct$OOB_MAE
resdHiAnnoyFitAB['All fluct', 'Rsquared'] <- resultsOutFluct$Rsquared
resdHiAnnoyPermImpAB$AllFluct <- resultsOutFluct$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=4}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutFluct.conimp <- arrange(resultsOutFluct$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutFluct.conimp) + geom_col(aes(x=factor(rownames(resultsOutFluct.conimp), levels=rownames(resultsOutFluct.conimp)), y=CondPermImp), fill=mycolours[4], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + ggtitle("All fluctuation strength") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdHiAnnoyAllFluctConPermimp.svg", width=8, height=4, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoyAllFluctConPermimp.svg")
  
  ggsave(filename="PtsABdHiAnnoyAllFluctConPermimp.pdf", width=8, height=4, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoyAllFluctConPermimp.pdf")
}

```

Selected metric

```{r}

allFluctVar <- "UASFluctECMA10ExBin"

```

##### dRoughness

###### Set variables

```{r}

# Roughness
iVars <- c(allVar, eventVar, ambVar, "dRoughECMA10ExBin", "dRoughECMA05ExBin", "dRoughFZ10ExMaxLR", "dRoughFZ05ExMaxLR", "UASRoughECMA10ExBin", "UASRoughECMA05ExBin", "UASRoughFZ10ExMaxLR", "UASRoughFZ05ExMaxLR", "UASRoughDW10ExMaxLR", "UASRoughDW05ExMaxLR")
dVar <- "dHighAnnoyPc"

seeds <- c(69851, 85109, 410986, 1563, 896)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 1501
mtry <- as.integer(length(iVars)/1.25)

```

###### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutRough <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutRough$OOB_RMSE
resultsOutRough$OOB_MAE
resultsOutRough$Rsquared

```

Train multiple seeds model

```{r}

resultsOutRough <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutRough$OOB_RMSE
resultsOutRough$OOB_MAE
resultsOutRough$Rsquared

```

```{r}
# store results
resdHiAnnoyFitAB['All rough', 'RMSE'] <- resultsOutRough$OOB_RMSE
resdHiAnnoyFitAB['All rough', 'MAE'] <- resultsOutRough$OOB_MAE
resdHiAnnoyFitAB['All rough', 'Rsquared'] <- resultsOutRough$Rsquared
resdHiAnnoyPermImpAB$AllRough <- resultsOutRough$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=4}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutRough.conimp <- arrange(resultsOutRough$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutRough.conimp) + geom_col(aes(x=factor(rownames(resultsOutRough.conimp), levels=rownames(resultsOutRough.conimp)), y=CondPermImp), fill=mycolours[5], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + ggtitle("All roughness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdHiAnnoyAllRoughConPermimp.svg", width=8, height=4, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoyAllRoughConPermimp.svg")
  
  ggsave(filename="PtsABdHiAnnoyAllRoughConPermimp.pdf", width=8, height=4, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoyAllRoughConPermimp.pdf")
}


```

Selected metric

```{r}

allRoughVar <- "dRoughFZ05ExMaxLR"

```

##### dImpulsiveness

###### Set variables

```{r}
# Impulsiveness
iVars <- c(allVar, eventVar, ambVar, "dImpulsSHMAvgMaxLR", "dImpulsSHM05ExMaxLR", "dImpulsSHMPowAvgMaxLR",
           "dImpulsLoudWZAvgMaxLR", "dImpulsLoudWZ05ExMaxLR", "dImpulsLoudWZPowAvgMaxLR",
           "dImpulsLoudWECMAAvgBin", "dImpulsLoudWECMA05ExBin", "dImpulsLoudWECMAPowAvgBin", "UASImpulsSHMAvgMaxLR", "UASImpulsSHM05ExMaxLR", "UASImpulsSHMPowAvgMaxLR", "UASImpulsLoudWZAvgMaxLR", "UASImpulsLoudWZ05ExMaxLR", "UASImpulsLoudWZPowAvgMaxLR", "UASImpulsLoudWECMAAvgBin", "UASImpulsLoudWECMA05ExBin", "UASImpulsLoudWECMAPowAvgBin")
dVar <- "dHighAnnoyPc"

seeds <- c(418659, 7805, 38475, 65834, 1653)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 1501
mtry <- as.integer(length(iVars)/1.75)

```


###### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutImpuls <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutImpuls$OOB_RMSE
resultsOutImpuls$OOB_MAE
resultsOutImpuls$Rsquared

```

Train multiple seeds model

```{r}

resultsOutImpuls <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutImpuls$OOB_RMSE
resultsOutImpuls$OOB_MAE
resultsOutImpuls$Rsquared

```

```{r}

# store results
resdHiAnnoyFitAB['All impuls', 'RMSE'] <- resultsOutImpuls$OOB_RMSE
resdHiAnnoyFitAB['All impuls', 'MAE'] <- resultsOutImpuls$OOB_MAE
resdHiAnnoyFitAB['All impuls', 'Rsquared'] <- resultsOutImpuls$Rsquared
resdHiAnnoyPermImpAB$AllImpuls <- resultsOutImpuls$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=5.6}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutImpuls.conimp <- arrange(resultsOutImpuls$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutImpuls.conimp) + geom_col(aes(x=factor(rownames(resultsOutImpuls.conimp), levels=rownames(resultsOutImpuls.conimp)), y=CondPermImp), fill=mycolours[6], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + ggtitle("All impulsiveness") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip()
pBar

if (saveplots){
  ggsave(filename="PtsABdHiAnnoyAllImpulsConPermimp.svg", width=8, height=5.6, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoyAllImpulsConPermimp.svg")
  
  ggsave(filename="PtsABdHiAnnoyAllImpulsConPermimp.pdf", width=8, height=5.6, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoyAllImpulsConPermimp.pdf")
}

```

Selected metric

```{r}

allImpulsVar <- "UASImpulsLoudWZAvgMaxLR"

```

#### dSQM and loudness comparison

Now the highest importance dSQMs are ranked against each other, controlling for loudness difference.

##### Include dtonal loudness

###### Set variables

```{r}

iVars <- c(allVar, eventVar, ambVar, allSharpVar, allTonLdVar, allFluctVar, allRoughVar, allImpulsVar)
dVar <- "dHighAnnoyPc"

seeds <- c(98465, 54163, 6541, 36485, 849675)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 1001
mtry <- 3

```


###### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutSQMs1 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs1$OOB_RMSE
resultsOutSQMs1$OOB_MAE
resultsOutSQMs1$Rsquared

```

Train multiple seeds model

```{r}

resultsOutSQMs1 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs1$OOB_RMSE
resultsOutSQMs1$OOB_MAE
resultsOutSQMs1$Rsquared

```

```{r}

# store results
resdHiAnnoyFitAB['All SQMs inc tonal loud', 'RMSE'] <- resultsOutSQMs1$OOB_RMSE
resdHiAnnoyFitAB['All SQMs inc tonal loud', 'MAE'] <- resultsOutSQMs1$OOB_MAE
resdHiAnnoyFitAB['All SQMs inc tonal loud', 'Rsquared'] <- resultsOutSQMs1$Rsquared
resdHiAnnoyPermImpAB$AllSQMs1 <- resultsOutSQMs1$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=2.4}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutSQMs1.conimp <- arrange(resultsOutSQMs1$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutSQMs1.conimp) + geom_col(aes(x=factor(rownames(resultsOutSQMs1.conimp), levels=rownames(resultsOutSQMs1.conimp)), y=CondPermImp), fill=mycolours[7], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 40))
pBar

if (saveplots){
  ggsave(filename="PtsABdHiAnnoyAllSQMsTonLdConPermimp.svg", width=8, height=2.4, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoyAllSQMsTonLdConPermimp.svg")
  
  ggsave(filename="PtsABdHiAnnoyAllSQMsTonLdConPermimp.pdf", width=8, height=2.4, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoyAllSQMsTonLdConPermimp.pdf")
}

```

##### Exclude tonal loudness

###### Set variables

```{r}

iVars <- c(allVar, eventVar, ambVar, allSharpVar, allTonalVar, allFluctVar, allRoughVar, allImpulsVar)
dVar <- "dHighAnnoyPc"

seeds <- c(49865, 7852, 845961, 410583, 36748)

```

###### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
             ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```

Selected hyperparameters

```{r}

ntree <- 2501
mtry <- as.integer(length(iVars)/1.6)

```

###### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutSQMs2 <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs2$OOB_RMSE
resultsOutSQMs2$OOB_MAE
resultsOutSQMs2$Rsquared

```

Train multiple seeds model

```{r}

resultsOutSQMs2 <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutSQMs2$OOB_RMSE
resultsOutSQMs2$OOB_MAE
resultsOutSQMs2$Rsquared

```

```{r}

# store results
resdHiAnnoyFitAB['All SQMs no tonal loud', 'RMSE'] <- resultsOutSQMs2$OOB_RMSE
resdHiAnnoyFitAB['All SQMs no tonal loud', 'MAE'] <- resultsOutSQMs2$OOB_MAE
resdHiAnnoyFitAB['All SQMs no tonal loud', 'Rsquared'] <- resultsOutSQMs2$Rsquared
resdHiAnnoyPermImpAB$AllSQMs2 <- resultsOutSQMs2$conditional_permimp

```

###### Plot results

```{r, fig.width=8,fig.height=2.4}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutSQMs2.conimp <- arrange(resultsOutSQMs2$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutSQMs2.conimp) + geom_col(aes(x=factor(rownames(resultsOutSQMs2.conimp), levels=rownames(resultsOutSQMs2.conimp)), y=CondPermImp), fill=mycolours[7], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 40))
pBar

if (saveplots){
  ggsave(filename="PtsABdHiAnnoyAllSQMsNoTonLdConPermimp.svg", width=8, height=2.4, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoyAllSQMsNoTonLdConPermimp.svg")
  
  ggsave(filename="PtsABdHiAnnoyAllSQMsNoTonLdConPermimp.pdf", width=8, height=2.4, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoyAllSQMsNoTonLdConPermimp.pdf")
}

```

#### dPsychoacoustic annoyance metrics

##### Set variables

```{r}

iVars <- c(ambVar, "dPsychAnnoyWidmann", "dPsychAnnoyMore", "dPsychAnnoyDi", "dPsychAnnoyTorija", "dPsychAnnoyWillemsen", "dPsychAnnoyBoucher", "UASPsychAnnoyWidmann", "UASPsychAnnoyMore", "UASPsychAnnoyDi", "UASPsychAnnoyTorija", "UASPsychAnnoyWillemsen", "UASPsychAnnoyBoucher")
dVar <- "dHighAnnoyPc"

seeds <- c(835702, 54, 470912, 652, 55297)


```

##### Hyperparameter tuning

```{r, fig.width=12, fig.height=4}

p <- mtryTune(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2],
              ntrees=ntrees, minsplit=minsplit, minbucket=minbucket)
p

```


Selected hyperparameters

```{r}

ntree <- 4001
mtry <-  as.integer(length(iVars)/1.25)

```

##### Run model

Train preliminary model

```{r}

nperm <- 5

resultsOutPA <- crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds[1:2], ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutPA$OOB_RMSE
resultsOutPA$OOB_MAE
resultsOutPA$Rsquared

```

Train multiple seeds model

```{r}

resultsOutPA <- multi_crfReg(dataIn=stimDataNum, iVars=iVars, dVar=dVar, seeds=seeds, ntree=ntree, mtry=mtry, permImpCondThres=permImpCondThres, nperm=nperm, minsplit=minsplit, minbucket=minbucket)

# print model prediction results
resultsOutPA$OOB_RMSE
resultsOutPA$OOB_MAE
resultsOutPA$Rsquared

```

```{r}

# store results
resdHiAnnoyFitAB['All Psychoacoustic annoyance', 'RMSE'] <- resultsOutPA$OOB_RMSE
resdHiAnnoyFitAB['All Psychoacoustic annoyance', 'MAE'] <- resultsOutPA$OOB_MAE
resdHiAnnoyFitAB['All Psychoacoustic annoyance', 'Rsquared'] <- resultsOutPA$Rsquared
resdHiAnnoyPermImpAB$AllPA <- resultsOutPA$conditional_permimp

```

##### Plot results

```{r, fig.width=8,fig.height=4.4}
par(mai=c(0,3,0,0))

# plot conditional importance
resultsOutPA.conimp <- arrange(resultsOutPA$conditional_permimp, desc(row_number()))

pBar <- ggplot(resultsOutPA.conimp) + geom_col(aes(x=factor(rownames(resultsOutPA.conimp), levels=rownames(resultsOutPA.conimp)), y=CondPermImp), fill=mycolours[10], width=0.5) + labs(x="Variable", y="Conditional variable permutation importance (% highly annoyed)") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 70))
pBar

if (saveplots){
  ggsave(filename="PtsABdHiAnnoyAllPAConPermimp.svg", width=8, height=4.4, path=file.path(outFigPath, "svg"))
  unlink("PtsABdHiAnnoyAllPAConPermimp.svg")
  
  ggsave(filename="PtsABdHiAnnoyAllPAConPermimp.pdf", width=8, height=4.4, path=file.path(outFigPath, "pdf"))
  unlink("PtsABdHiAnnoyAllPAConPermimp.pdf")
}

```

### Save the results outputs to file

```{r}

if (savedata){
  utils::write.csv(resdHiAnnoyFitAB, paste(outDataPath, "\\PtsABCRFdHiAnnoyOOBFit.csv", sep=""))
  ii <- 0
  temp = list()
  for (res in resdHiAnnoyPermImpAB){
    ii <- ii + 1
    temp[[ii]] <- as.data.frame(resdHiAnnoyPermImpAB[ii])
    names(temp[[ii]]) <- names(resdHiAnnoyPermImpAB[ii])
  }
  openxlsx::write.xlsx(temp, paste(outDataPath, "\\PtsABCRFdHiAnnoyConPermimp.xlsx",
                                   sep=""),
                       rowNames=TRUE)
}

```


## Parts A&B summary

Summary of results for Parts A & B combined

### With tonal loudness

#### Absolute variables

```{r, fig.width=4, fig.height=4}
# combine the annoyance perm importance results

# convert each result to a tibble with rownames added to a column, renaming the data column to 'dAnnoy' etc.
resdAnnoyMnAbsPermImpTblAB <- as.data.frame(resdAnnoyMnPermImpAB$AbsSQMs1/max(resdAnnoyMnPermImpAB$AbsSQMs1)) |>
  tibble::rownames_to_column(var='Variable')
colnames(resdAnnoyMnAbsPermImpTblAB)[2] <- "dAnnoy"

resdHiAnnoyAbsPermImpTblAB <- as.data.frame(resdHiAnnoyPermImpAB$AbsSQMs1/max(resdHiAnnoyPermImpAB$AbsSQMs1)) |>
  tibble::rownames_to_column(var='Variable')
colnames(resdHiAnnoyAbsPermImpTblAB)[2] <- "dHiAnnoy"

# merge the dataframes
resAbsPermImpTblAB <- list(resdAnnoyMnAbsPermImpTblAB, resdHiAnnoyAbsPermImpTblAB) |>
  purrr::reduce(merge, by = c('Variable'), all = T)

# rename the columns
colnames(resAbsPermImpTblAB)[2:3] <- c("Mean change in annoyance", "%HA | HA' (amb.)")
resAbsPermImpTblAB[is.na(resAbsPermImpTblAB)] <- 0

resAbsAB <- tidyr::pivot_longer(resAbsPermImpTblAB, cols=-Variable, names_to="Outcome", values_to="Imp")

# reorder res tibble, descending by the variable Imp grouped sum and create column with new group order as a factor
resAbsAB <- resAbsAB |> mutate(Variable_sum = sum(Imp), .by=Variable) |> arrange(desc(Variable_sum)) |> group_by(Variable_sum, Variable) |>
   mutate(Order = cur_group_id()) |> mutate(Order = as.factor(Order)) |> arrange(desc(Order))

# Reorder outcome levels
resAbsAB$Outcome <- factor(resAbsAB$Outcome, levels=c("Mean change in annoyance", "%HA | HA' (amb.)"))

# plot res as horizontal bar chart, with Imp as y axis, Variable as x axis, Outcome as fill, and Variable_sum as order, relabel x axis with Variable names
pBar <- ggplot(resAbsAB) + geom_col(aes(fill=Outcome, y=Imp, x=Order), colour='grey35', linewidth=0,  width=0.75, show.legend=TRUE) + labs(x="Variable", y="Normalised conditional variable\npermutation importance") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2)) + coord_flip(ylim=c(0, 1)) + scale_fill_manual(values=mycolours, labels=c(expression(paste(bar(Delta~A))), "%HA | HA' (amb.)")) + scale_x_discrete(labels=unique(rev(resAbsAB$Variable))) + guides(fill=guide_legend(title='Outcome'))
pBar + scale_y_continuous(breaks=seq(0, 1, by=0.5))

if (saveplots){
  ggsave(filename="PtsABcrfAbsSQMsSummary.svg", width=8, height=4, path=file.path(outFigPath, "svg"))
  unlink("PtsABcrfAbsSQMsSummary.svg")

  ggsave(filename="PtsABcrfAbsSQMsSummary.pdf", width=8, height=4, path=file.path(outFigPath, "pdf"))
  unlink("PtsABcrfAbsSQMsSummary.pdf")
}


# plot res as horizontal bar chart, with Imp as y axis, Variable as x axis, Outcome as fill, and Variable_sum as order, relabel x axis with Variable names
pBar <- ggplot(resAbsAB) + geom_col(aes(fill=Outcome, y=Imp, x=Order), colour='grey35', linewidth=0,  width=0.75, show.legend=TRUE) + labs(x="Variable", y="Normalised conditional variable\npermutation importance") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2), legend.position = "top") + coord_flip(ylim=c(0, 1)) + scale_fill_manual(values=mycolours, labels=c(expression(paste(bar(Delta~A))), "%HA | HA' (amb.)")) + scale_x_discrete(labels=unique(rev(resAbsAB$Variable))) + guides(fill=guide_legend(title='Outcome', nrow=2, ncol=1))
pBar + scale_y_continuous(breaks=seq(0, 1, by=0.5))

if (saveplots){
  ggsave(filename="PtsABcrfAbsSQMsSummaryNw.svg", width=4, height=4, path=file.path(outFigPath, "svg"))
  unlink("PtsABcrfAbsSQMsSummary.svg")

  ggsave(filename="PtsABcrfAbsSQMsSummaryNw.pdf", width=4, height=4, path=file.path(outFigPath, "pdf"))
  unlink("PtsABcrfAbsSQMsSummary.pdf")
}

```

#### All variables

```{r, fig.width=8, fig.height=4}
# combine the annoyance perm importance results

# convert each result to a tibble with rownames added to a column, renaming the data column to 'dAnnoy' etc.
resdAnnoyMnAllPermImpTblAB <- as.data.frame(resdAnnoyMnPermImpAB$AllSQMs1/max(resdAnnoyMnPermImpAB$AllSQMs1)) |>
  tibble::rownames_to_column(var='Variable')
colnames(resdAnnoyMnAllPermImpTblAB)[2] <- "dAnnoy"

resdHiAnnoyAllPermImpTblAB <- as.data.frame(resdHiAnnoyPermImpAB$AllSQMs1/max(resdHiAnnoyPermImpAB$AllSQMs1)) |>
  tibble::rownames_to_column(var='Variable')
colnames(resdHiAnnoyAllPermImpTblAB)[2] <- "dHiAnnoy"

# merge the dataframes
resAllPermImpTblAB <- list(resdAnnoyMnAllPermImpTblAB, resdHiAnnoyAllPermImpTblAB) |>
  purrr::reduce(merge, by = c('Variable'), all = T)

# rename the columns
colnames(resAllPermImpTblAB)[2:3] <- c("Mean change in annoyance", "%HA | HA' (amb.)")
resAllPermImpTblAB[is.na(resAllPermImpTblAB)] <- 0

resAllAB <- tidyr::pivot_longer(resAllPermImpTblAB, cols=-Variable, names_to="Outcome", values_to="Imp")

# reorder res tibble, descending by the variable Imp grouped sum and create column with new group order as a factor
resAllAB <- resAllAB |> mutate(Variable_sum = sum(Imp), .by=Variable) |> arrange(desc(Variable_sum)) |> group_by(Variable_sum, Variable) |>
   mutate(Order = cur_group_id()) |> mutate(Order = as.factor(Order)) |> arrange(desc(Order))

# Reorder outcome levels
resAllAB$Outcome <- factor(resAllAB$Outcome, levels=c("Mean change in annoyance", "%HA | HA' (amb.)"))

# plot res as horizontal bar chart, with Imp as y axis, Variable as x axis, Outcome as fill, and Variable_sum as order, relabel x axis with Variable names
pBar <- ggplot(resAllAB) + geom_col(aes(fill=Outcome, y=Imp, x=Order), colour='grey35', linewidth=0,  width=0.75, show.legend=TRUE) + labs(x="Variable", y="Normalised conditional variable permutation importance") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2), legend.position = "right") + coord_flip(ylim=c(-0.1, 1.1)) + scale_fill_manual(values=mycolours) + scale_x_discrete(labels=unique(rev(resAllAB$Variable)))
pBar + scale_y_continuous(breaks=seq(0, 1, by=0.5))

if (saveplots){
  ggsave(filename="PtsABcrfAllSQMsSummary.svg", width=8, height=3, path=file.path(outFigPath, "svg"))
  unlink("PtsABcrfAllSQMsSummary.svg")
  
  ggsave(filename="PtsABcrfAllSQMsSummary.pdf", width=8, height=3, path=file.path(outFigPath, "pdf"))
  unlink("PtsABcrfAllSQMsSummary.pdf")
}

```

### No tonal loudness

#### Absolute variables

```{r, fig.width=8, fig.height=4}
# combine the annoyance perm importance results

# convert each result to a tibble with rownames added to a column, renaming the data column to 'dAnnoy' etc.
resdAnnoyMnAbsPermImpNoTonLdTblAB <- as.data.frame(resdAnnoyMnPermImpAB$AbsSQMs2/max(resdAnnoyMnPermImpAB$AbsSQMs2)) |>
  tibble::rownames_to_column(var='Variable')
colnames(resdAnnoyMnAbsPermImpNoTonLdTblAB)[2] <- "dAnnoy"

resdHiAnnoyAbsPermImpNoTonLdTblAB <- as.data.frame(resdHiAnnoyPermImpAB$AbsSQMs2/max(resdHiAnnoyPermImpAB$AbsSQMs2)) |>
  tibble::rownames_to_column(var='Variable')
colnames(resdHiAnnoyAbsPermImpNoTonLdTblAB)[2] <- "dHiAnnoy"

# merge the dataframes
resAbsPermImpNoTonLdTblAB <- list(resdAnnoyMnAbsPermImpNoTonLdTblAB, resdHiAnnoyAbsPermImpNoTonLdTblAB) |>
  purrr::reduce(merge, by = c('Variable'), all = T)

# rename the columns
colnames(resAbsPermImpNoTonLdTblAB)[2:3] <- c("Mean change in annoyance", "%HA | HA' (amb.)")
resAbsPermImpNoTonLdTblAB[is.na(resAbsPermImpNoTonLdTblAB)] <- 0

resAbsNoTonLdAB <- tidyr::pivot_longer(resAbsPermImpNoTonLdTblAB, cols=-Variable, names_to="Outcome", values_to="Imp")

# reorder res tibble, descending by the variable Imp grouped sum and create column with new group order as a factor
resAbsNoTonLdAB <- resAbsNoTonLdAB |> mutate(Variable_sum = sum(Imp), .by=Variable) |> arrange(desc(Variable_sum)) |> group_by(Variable_sum, Variable) |>
   mutate(Order = cur_group_id()) |> mutate(Order = as.factor(Order)) |> arrange(desc(Order))

# Reorder outcome levels
resAbsNoTonLdAB$Outcome <- factor(resAbsNoTonLdAB$Outcome, levels=c("Mean change in annoyance", "%HA | HA' (amb.)"))

# plot res as horizontal bar chart, with Imp as y axis, Variable as x axis, Outcome as fill, and Variable_sum as order, relabel x axis with Variable names
pBar <- ggplot(resAbsNoTonLdAB) + geom_col(aes(fill=Outcome, y=Imp, x=Order), colour='grey35', linewidth=0,  width=0.75, show.legend=TRUE) + labs(x="Variable", y="Normalised conditional variable permutation importance") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2), legend.position = "right") + coord_flip(ylim=c(-0.1, 1.1)) + scale_fill_manual(values=mycolours) + scale_x_discrete(labels=unique(rev(resAbsNoTonLdAB$Variable)))
pBar + scale_y_continuous(breaks=seq(0, 1, by=0.5))

if (saveplots){
  ggsave(filename="PtsABcrfAbsSQMsNoTonLdSummary.svg", width=8, height=3, path=file.path(outFigPath, "svg"))
  unlink("PtsABcrfAbsSQMsNoTonLdSummary.svg")
  
  ggsave(filename="PtsABcrfAbsSQMsNoTonLdSummary.pdf", width=8, height=3, path=file.path(outFigPath, "pdf"))
  unlink("PtsABcrfAbsSQMsNoTonLdSummary.pdf")
}

```

#### All variables

```{r, fig.width=8, fig.height=4}
# combine the annoyance perm importance results

# convert each result to a tibble with rownames added to a column, renaming the data column to 'dAnnoy' etc.
resdAnnoyMnAllPermImpNoTonLdTblAB <- as.data.frame(resdAnnoyMnPermImpAB$AllSQMs2/max(resdAnnoyMnPermImpAB$AllSQMs2)) |>
  tibble::rownames_to_column(var='Variable')
colnames(resdAnnoyMnAllPermImpNoTonLdTblAB)[2] <- "dAnnoy"

resdHiAnnoyAllPermImpNoTonLdTblAB <- as.data.frame(resdHiAnnoyPermImpAB$AllSQMs2/max(resdHiAnnoyPermImpAB$AllSQMs2)) |>
  tibble::rownames_to_column(var='Variable')
colnames(resdHiAnnoyAllPermImpNoTonLdTblAB)[2] <- "dHiAnnoy"

# merge the dataframes
resAllPermImpNoTonLdTblAB <- list(resdAnnoyMnAllPermImpNoTonLdTblAB, resdHiAnnoyAllPermImpNoTonLdTblAB) |>
  purrr::reduce(merge, by = c('Variable'), all = T)

# rename the columns
colnames(resAllPermImpNoTonLdTblAB)[2:3] <- c("Mean change in annoyance", "%HA | HA' (amb.)")
resAllPermImpNoTonLdTblAB[is.na(resAllPermImpNoTonLdTblAB)] <- 0

resAllNoTonLdAB <- tidyr::pivot_longer(resAllPermImpNoTonLdTblAB, cols=-Variable, names_to="Outcome", values_to="Imp")

# reorder res tibble, descending by the variable Imp grouped sum and create column with new group order as a factor
resAllNoTonLdAB <- resAllNoTonLdAB |> mutate(Variable_sum = sum(Imp), .by=Variable) |> arrange(desc(Variable_sum)) |> group_by(Variable_sum, Variable) |>
   mutate(Order = cur_group_id()) |> mutate(Order = as.factor(Order)) |> arrange(desc(Order))

# Reorder outcome levels
resAllNoTonLdAB$Outcome <- factor(resAllNoTonLdAB$Outcome, levels=c("Mean change in annoyance", "%HA | HA' (amb.)"))

# plot res as horizontal bar chart, with Imp as y axis, Variable as x axis, Outcome as fill, and Variable_sum as order, relabel x axis with Variable names
pBar <- ggplot(resAllNoTonLdAB) + geom_col(aes(fill=Outcome, y=Imp, x=Order), colour='grey35', linewidth=0,  width=0.75, show.legend=TRUE) + labs(x="Variable", y="Normalised conditional variable permutation importance") + theme(text = element_text(family = "serif"), panel.grid=element_line(color = rgb(235, 235, 235, 100, maxColorValue = 255), linewidth = 0.25, linetype = 2), legend.position = "right") + coord_flip(ylim=c(-0.1, 1.1)) + scale_fill_manual(values=mycolours) + scale_x_discrete(labels=unique(rev(resAllNoTonLdAB$Variable)))
pBar + scale_y_continuous(breaks=seq(0, 1, by=0.5))

if (saveplots){
  ggsave(filename="PtsABcrfAllSQMsNoTonLdSummary.svg", width=8, height=3, path=file.path(outFigPath, "svg"))
  unlink("PtsABcrfAllSQMsNoTonLdSummary.svg")
  
  ggsave(filename="PtsABcrfAllSQMsNoTonLdSummary.pdf", width=8, height=3, path=file.path(outFigPath, "pdf"))
  unlink("PtsABcrfAllSQMsNoTonLdSummary.pdf")
}

```

### Save the results outputs to file

```{r}
# Make a list of the summary results
resSummary <- list(resAbsAB, resAllAB, resAbsNoTonLdAB, resAllNoTonLdAB)

# Save the results
if (savedata){
  ii <- 0
  temp = list()
  for (res in resSummary){
    ii <- ii + 1
    temp[[ii]] <- data.frame(resSummary[ii])
  }
  openxlsx::write.xlsx(temp, paste(outDataPath, "\\PtsABCRFSummary.xlsx",
                                   sep=""),
                       rowNames=TRUE)
}

```